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2.2: Combinations and Compositions of Functions
2: Building New Functions
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Sun, 02 Jul 2023 23:00:46 GMT
2.2.1: Exercises - Combinations of Functions
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Jason Gardner
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Precalculus: Describing Relationships Between Quantities in the World Around Us
2: Building New Functions
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2.2.1: Exercises - Combinations of Functions
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11: Biochemistry of the Genome (Part C)
Genetic differences among related microbes dictate many observed biochemical and virulence differences. For example, some strains of the bacterium Escherichia coli are harmless members of the normal microbiota in the human gastrointestinal tract. Other strains of the same species have genes that give them the ability to cause disease. In bacteria, such genes are not inherited via sexual reproduction, as in humans. Often, they are transferred via plasmids, small circular pieces of double-stranded DNA that can be exchanged between prokaryotes.
-
- 11.1: Using Microbiology to Discover the Secrets of Life
- DNA was discovered and characterized long before its role in heredity was understood. Microbiologists played significant roles in demonstrating that DNA is the hereditary information found within cells. In the 1850s and 1860s, Gregor Mendel experimented with true-breeding garden peas to demonstrate the heritability of specific observable traits. In 1869, Friedrich Miescher isolated and purified a compound rich in phosphorus from the nuclei of white blood cells; he named the compound nuclein.
Thumbnail: In the laboratory, the double helix can be denatured to single-stranded DNA through exposure to heat or chemicals, and then renatured through cooling or removal of chemical denaturants to allow the DNA strands to reanneal. (credit: modification of work by Hernández-Lemus E, Nicasio-Collazo LA, Castañeda-Priego R)


7: The Normal Distribution
In this chapter, you will study the normal distribution, the standard normal distribution, and applications associated with them. The normal distribution has two parameters (two numerical descriptive measures), the mean (μ) and the standard deviation (σ).
-
- 7.1: Prelude to The Normal Distribution
- The normal, a continuous distribution, is the most important of all the distributions. It is widely used and even more widely abused. Its graph is bell-shaped. In this chapter, you will study the normal distribution, the standard normal distribution, and applications associated with them. The normal distribution has two parameters (two numerical descriptive measures), the mean (μ) and the standard deviation (σ).
-
- 7.2: The Standard Normal Distribution
- A z-score is a standardized value. Its distribution is the standard normal, Z∼N(0,1). The mean of the z-scores is zero and the standard deviation is one. If y is the z-score for a value x from the normal distribution N(μ,σ) then z tells you how many standard deviations x is above (greater than) or below (less than) μ.
-
- 7.3: Using the Normal Distribution
- The normal distribution, which is continuous, is the most important of all the probability distributions. Its graph is bell-shaped. This bell-shaped curve is used in almost all disciplines. Since it is a continuous distribution, the total area under the curve is one. The parameters of the normal are the mean μ and the standard deviation σ. A special normal distribution, called the standard normal distribution is the distribution of z-scores. Its mean is zero, and its standard deviation is one.


Describe the characteristics of the normal distribution.
Apply the 68-95-99.7 percent groups to normal distribution datasets.
Use the normal distribution to calculate a
-score.
Find and interpret percentiles and quartiles.
Many datasets that result from natural phenomena tend to have histograms that are symmetric and bell-shaped. Imagine finding yourself with a whole lot of time on your hands, and nothing to keep you entertained but a coin, a pencil, and paper. You decide to flip that coin 100 times and record the number of heads. With nothing else to do, you repeat the experiment ten times total. Using a computer to simulate this series of experiments, here’s a sample for the number of heads in each trial:
54, 51, 40, 42, 53, 50, 52, 52, 47, 54
It makes sense that we’d get somewhere around 50 heads when we flip the coin 100 times, and it makes sense that the result won’t
always
be exactly 50 heads. In our results, we can see numbers that were generally near 50 and not always 50, like we thought.
Moving Toward Normality
Let’s take a look at a histogram for the dataset in our section opener:
This is interesting, but the data seem pretty sparse. There were no trials where you saw between 43 and 47 heads, for example. Those results don’t seem impossible; we just didn’t flip enough times to give them a chance to pop up. So, let’s do it again, but this time we'll perform 100 coin flips 100 times. Rather than review all 100 results, which could be overwhelming, let's instead visualize the resulting histogram.
From the histogram, we see that most of the trials resulted in between, say, 44 and 56 heads. There were some more unusual results: one trial resulted in 70 heads, which seems really unlikely (though still possible!). But we’re starting to maybe get a sense of the distribution. More data would help, though. Let’s simulate another 900 trials and add them to the histogram!
We can still see that 70 is a really unusual observation, though we came close in another trial (one that had 68 heads). Now, the distribution is coming more into focus: It looks quite symmetric and bell-shaped. Let’s just go ahead and take this thought experiment to an extreme conclusion: 10,000 trials.
The distribution is pretty clear now. Distributions that are symmetric and bell-shaped like this pop up in all sorts of natural phenomena, such as the heights of people in a population, the circumferences of eggs of a particular bird species, and the numbers of leaves on mature trees of a particular species. All of these have bell-shaped distributions. Additionally, the results of many types of repeated experiments generally follow this same pattern, as we saw with the coin-flipping example; this fact is the basis for much of the work done by statisticians. It’s a fact that’s important enough to have its own name: the Central Limit Theorem.
People in Mathematics:
John Kerrich
Having enough time on your hands to actually perform this coin-flipping experiment may sound far-fetched, but the English mathematician John Kerrich found himself in just such a situation. While he was studying abroad in Denmark in 1940, that country was invaded by the Germans. Kerrich was captured and placed in an internment camp, where he remained for the duration of the war. Kerrich knew that he had all kinds of time on his hands. He also studied statistics, so he knew what should happen
theoretically
if he flipped a coin many, many times. He also knew of nobody who had ever
tested
that theory with an actual, large-scale experiment. So, he did it: While he was incarcerated, Kerrich flipped a regular coin 10,000 times and recorded the results. Sure enough, the theory held up!
The Normal Distribution
In the coin flipping example above, the distribution of the number of heads for 10,000 trials was close to perfectly symmetric and bell-shaped:
Because distributions with this shape appear so often, we have a special name for them:
normal distributions
. Normal distributions can be completely described using two numbers we’ve seen before: the
mean
of the data and the
standard deviation
of the data. You may remember that we described the mean as a measure of centrality; for a normal distribution, the mean tells us exactly where the center of the distribution falls. The peak of the distribution happens at the mean (and, because the distribution is symmetric, it’s also the median). The standard deviation is a measure of dispersion; for a normal distribution, it tells us how spread out the histogram looks. To illustrate these points, let’s look at some examples.
Example \(\PageIndex{1}\): Identifying the Mean of a Normal Distribution
This graph shows three normal distributions. What are their means?
Answer
Step 1:
Take a look at the three curves on the graph. Since the mean of a normal distribution occurs at the peak, we should look for the highest point on each distribution. Let’s draw a line from each curve's peak down to the axis, so we can see where these peaks occur:
Your Turn \(\PageIndex{1}\)
Identify the means of these three distributions:
Figure
\(\PageIndex{8}\)
Example \(\PageIndex{2}\): Identifying the Standard Deviation of a Normal Distribution
This graph shows three distributions, all with mean 2. What are their standard deviations?
Figure
\(\PageIndex{9}\)
Answer
Step 1:
Identifying the standard deviation from a graph can be a little bit tricky. Let’s focus in on the yellow (lowest peaked) curve:
Figure
\(\PageIndex{10}\)
Step 2:
Notice that the graph curves downward in the middle, and curves upwards on the ends. Highlighted in red is the part that curves downward and in green, the part that curves upward:
Figure
\(\PageIndex{11}\)
Step 3:
The places where the graph changes from curving up to curving down (or vice versa) are called
inflection points
. Let’s identify where those occur by dropping a line straight down from each:
Figure
\(\PageIndex{12}\)
Step 4:
We can estimate that the inflection points occur at
and
; the mean is at
(as shown by the middle dotted line). The difference between the mean and the location of either inflection point is the standard deviation; since
, we conclude that the standard deviation of the green distribution is 3.
Step 5:
Now, looking at the other two graphs, let's first identify the inflection points:
Figure
\(\PageIndex{13}\)
Step 6:
The red (tallest peaked) distribution has inflection points at 1 and 3, and the mean is 2. Thus, the standard deviation of the red distribution is 3 = 2 - 1 = 1 3 - 2 = 2 - 1 = 1 . The blue (lower peaked) distribution has inflection points at 0 and 4, and its mean is also 2. So, the standard deviation of the blue distribution is 2.
Your Turn \(\PageIndex{2}\)
Estimate the standard deviations of this normal distribution, centered at 5:
Figure
\(\PageIndex{14}\)
Let’s put it all together to identify a completely unknown normal distribution.
Example \(\PageIndex{3}\): Identifying the Mean and Standard Deviation of a Normal Distribution
Using the graph, identify the mean and standard deviation of the normal distribution.
Figure
\(\PageIndex{15}\)
Answer
Step 1:
Let’s start by putting dots on the graph at the peak and at the inflection points, then drop lines from those points straight down to the axis:
Figure
\(\PageIndex{16}\)
Step 2: From the red (middle) line, we can see that the mean of this distribution is 55. The blue (outermost) lines are each 3 units away from the mean (at 52 and 58), so the standard deviation is 3.
Your Turn \(\PageIndex{3}\)
Identify the mean and standard deviation of this distribution. Anything within 5 on the standard deviation is acceptable.
Figure
\(\PageIndex{17}\)
Properties of Normal Distributions: The 68-95-99.7 Rule
The most important property of normal distributions is tied to its standard deviation. If a dataset is perfectly normally distributed, then 68% of the data values will fall within one standard deviation of the mean. For example, suppose we have a set of data that follows the normal distribution with mean 400 and standard deviation 100. This means 68% of the data would fall between the values of 300 (one standard deviation
below
the mean:
) and 500 (one standard deviation
above
the mean:
). Looking at the histogram below, the shaded area represents 68% of the total area under the graph and above the axis:
Figure
\(\PageIndex{18}\)
Since 68% of the area is in the shaded region, this means that
of the area is found in the unshaded regions. We know that the distribution is symmetric, so that 32% must be divided evenly into the two unshaded tails: 16% in each.
Of course, datasets in the real world are never perfect; when dealing with actual data that seem to follow a symmetric, bell-shaped distribution, we’ll give ourselves a little bit of wiggle room and say that
approximately
68% of the data fall within one standard deviation of the mean.
The rule for one standard deviation can be extended to two standard deviations. Approximately 95% of a normally distributed dataset will fall within 2 standard deviations of the mean. If the mean is 400 and the standard deviation is 100, that means 95% calculation describes the way we compute standardized scores. (two standard deviations
below
the mean:
) and 600 (two standard deviations
above
the mean:
). We can visualize this in the following histogram:
Figure
\(\PageIndex{19}\)
As before, since 95% of the data are in the shaded area, that leaves 5% of the data to go into the unshaded tails. Since the histogram is symmetric, half of the 5% (that’s 2.5%) is in each.
We can even take this one step further: 99.7% of normally distributed data fall within 3 standard deviations of the mean. In this example, we’d see 99.7% of the data between 100 (calculated as
) and 700 (calculated as
). We can see this in the histogram below, although you may need to squint to find the unshaded bits in the tails!
Figure
\(\PageIndex{20}\)
This observation is formally known as the
68-95-99.7 Rule
.
If data are normally distributed with mean 8 and standard deviation 2, what percent of the data falls between 4 and 12?
If data are normally distributed with mean 25 and standard deviation 5, what percent of the data falls between 20 and 30?
If data are normally distributed with mean 200 and standard deviation 15, what percent of the data falls between 155 and 245?
Answer
Let’s look at a table that sets out the data values that are even multiples of the standard deviation (SD) above and below the mean:
3
×
SD
mean
-
3
×
SD
2
×
SD
mean
-
2
×
SD
1
×
SD
mean
-
1
×
SD
Mean
1
×
SD
mean
+
1
×
SD
2
×
SD
mean
+
2
×
SD
3
×
SD
mean
+
3
×
SD
2
4
6
8
10
12
14
Since 4 and 12 represent two standard deviations above and below the mean, we conclude that 95% of the data will fall between them.
Let’s build another table:
3
×
SD
mean
-
3
×
SD
2
×
SD
mean
-
2
×
SD
1
×
SD
mean
-
1
×
SD
Mean
1
×
SD
mean
+
1
×
SD
2
×
SD
mean
+
2
×
SD
3
×
SD
mean
+
3
×
SD
10
15
20
25
30
35
40
We can see that 20 and 30 represent one standard deviation above and below the mean, so 68% of the data fall in that range.
Let’s make one more table:
3
×
SD
mean
-
3
×
SD
2
×
SD
mean
-
2
×
SD
1
×
SD
mean
−
1
×
SD
Mean
1
×
SD
mean
+
1
×
SD
2
×
SD
mean
+
2
×
SD
3
×
SD
mean
+
3
×
SD
155
170
185
200
215
230
245
Since 155 and 245 are three standard deviations above and below the mean, we know that 99.7% of the data will fall between them.
Your Turn \(\PageIndex{4}\)
If data are distributed normally with mean 0 and standard deviation 3, what percent of the data fall between –9 and 9?
If data are distributed normally with mean 50 and standard deviation 10, what percent of the data fall between 30 and 70?
If data are distributed normally with mean 60 and standard deviation 5, what percent of the data fall between 55 and 65?
If data are distributed normally with mean 100 and standard deviation 20, between what two values will 68% of the data fall?
If data are distributed normally with mean 0 and standard deviation 15, between what two values will 95% of the data fall?
If data are distributed normally with mean 14 and standard deviation 2, between what two values will 99.7% of the data fall?
Answer
The 68-95-99.7 Rule tells us that 68% of the data will fall within one standard deviation of the mean. So, to find the values we seek, we’ll add and subtract one standard deviation from the mean:
and
. Thus, we know that 68% of the data fall between 80 and 120.
Using the 68-95-99.7 Rule again, we know that 95% of the data will fall within 2 standard deviations of the mean. Let’s add and subtract two standard deviations from that mean:
and
. So, 95% of the data will fall between -30 and 30.
Once again, the 68-95-99.7 Rule tells us that 99.7% of the data will fall within three standard deviations of the mean. So, let’s add and subtract three standard deviations from the mean:
and
. Thus, we conclude that 99.7% of the data will fall between 8 and 20.
Your Turn \(\PageIndex{5}\)
If data are distributed normally with mean 70 and standard deviation 5, between what two values will 68% of the data fall?
If data are distributed normally with mean 40 and standard deviation 7, between what two values will 95% of the data fall?
If data are distributed normally with mean 200 and standard deviation 30, between what two values will 99.7% of the data fall?
There are more problems we can solve using the 68-95-99.7 Rule. but first we must understand what the rule implies. Remember, the rule says that 68% of the data falls within one standard deviation of the mean. Thus, with normally distributed data with mean 100 and standard deviation 10, we have this distribution:
Figure
\(\PageIndex{21}\)
Since we know that 68% of the data lie within one standard deviation of the mean, the implication is that 32% of the data must fall
beyond
one standard deviation away from the mean. Since the histogram is symmetric, we can conclude that half of the 32% (or 16%) is more than one standard deviation
above
the mean and the other half is more than one standard deviation
below
the mean:
Figure
\(\PageIndex{22}\)
Further, we know that the middle 68% can be split in half at the peak of the histogram, leaving 34% on either side:
Figure
\(\PageIndex{23}\)
So, just the “68” part of the 68-95-99.7 Rule gives us four other proportions in addition to the 68% in the rule. Similarly, the “95” and “99.7” parts each give us four more proportions:
Figure
\(\PageIndex{24}\)
Figure \(\PageIndex{25}\): Copy and Paste Caption here. (Copyright; author via source)
We can put all these together to find even more complicated proportions. For example, since the proportion between 100 and 120 is 47.5% and the proportion between 100 and 110 is 34%, we can subtract to find that the proportion between 110 and 120 is
:
Figure
\(\PageIndex{26}\)
Assume that we have data that are normally distributed with mean 80 and standard deviation 3.
What proportion of the data will be greater than 86?
What proportion of the data will be between 74 and 77?
What proportion of the data will be between 74 and 83?
Answer
Before we can answer these questions, we must mark off sections that are multiples of the standard deviation away from the mean:
Figure
\(\PageIndex{27}\)
To figure out what proportion of the data will be greater than 86, let's start by shading in the area of data that are above 86 in our figure, or the data more than two standard deviations above the mean.
Figure
\(\PageIndex{28}\)
We saw in Figure 8.51 that this proportion is 2.5%.
To figure out what proportion of the data will be between 74 and 77, let's start by shading in that area of data. These are data that are more than one but less than two standard deviations below the mean.
Figure
\(\PageIndex{29}\)
From Figure 8.51, we know that the proportion of data less than two standard deviations below the mean is 47.5%. And, from YOUR TURN 8.33, we know that 34% of the data is less than one standard deviation below the mean:
Figure
\(\PageIndex{30}\)
Subtracting, we see that the proportion of data between 74 and 77 is 13.5%.
To figure out what proportion of the data will be between 74 and 83, let's start by shading in that area of data in our figure.
Figure
\(\PageIndex{31}\)
Next, we'll break this region into two pieces at the mean:
Figure
\(\PageIndex{32}\)
From Figure 8.51, we know the blue (leftmost) region represents 47.5% of the data. And, using YOUR TURN 8.33, we get that the red (rightmost) region covers 34% of the data. Adding those together, the proportion we want is 81.5%.
Your Turn \(\PageIndex{6}\)
Suppose we have data that are normally distributed with mean 500 and standard deviation 100. What proportions of the data fall in these ranges?
300 to 500
600 to 800
400 to 700
Standardized Scores
When we want to apply the 68-95-99.7 Rule, we must first figure out how many standard deviations above or below the mean our data fall. This calculation is common enough that it has its own name: the
standardized score
. Values above the mean have positive standardized scores, while those below the mean have negative standardized scores. Since it's common to use the letter
to represent a standard score, this value is also often referred to as a
-score
.
So far, we’ve only really considered
-scores that are whole numbers, but in general they can be any number at all. For example, if we have data that are normally distributed with mean 80 and standard deviation 6, the value 85 is five units above the mean, which is less than one standard deviation. Dividing by the standard deviation, we get
. Since 85 is
of one standard deviation above the mean, we’d say that the standardized score for 85 is
5
6
z
=
5
6
(which is positive, since
). This calculation describes the way we compute standardized scores.
FORMULA
If \(x\) is a member of a normally distributed dataset with mean \(\mu\) and standard deviation \(\sigma\), then the standardized score for \(x\) is
\[z=\frac{x-\mu}{\sigma} . \nonumber \]
If you know a \(z\)-score but not the original data value \(x\), you can find it by solving the previous equation for \(x\) :
\[x=\mu+z \times \sigma . \nonumber \]
z
×
σ
.
Checkpoint
The symbols
μ
μ
and
σ
σ
are the Greek letters
mu
and
sigma
. They are the analogues of the English letters
and
, which stand for
mean
and
standard deviation
.
If you convert every data value in a dataset into its
-score, the resulting set of data will have mean 0 and standard deviation 1. This is why we call these
standardized scores
: the normal distribution with mean 0 and standard deviation 1 is often called the
standard normal distribution
.
Example \(\PageIndex{7}\): Standardizing Data
Suppose we have data that are normally distributed with mean 50 and standard deviation 6. Compute the standardized scores (rounded to three decimal places) for these data values:
52
40
68
Answer
For each of these, we'll plug the given values into the formula. Remember, the mean is \(\mu=50\) and the standard deviation is \(\sigma=6\) :
Suppose we have data that are normally distributed with mean 75 and standard deviation 5. Compute standardized scores for each of these data values: 66, 83, and 72.
Example \(\PageIndex{8}\): Converting Standardized Scores to Original Values
Suppose we have data that are normally distributed with mean 10 and standard deviation 2. Convert the following standardized scores into data values.
1.4
−0.9
3.5
Answer
We'll use the formula previously introduced to convert \(z\)-scores into \(x\)-values. In this case, the mean is \(\mu=10\) and the standard deviation is \(\sigma=2\) :
Suppose you have a normally distributed dataset with mean 2 and standard deviation 20. Convert these standardized scores to data values: –2.3, 1.4, and 0.2.
Using Google Sheets to Find Normal Percentiles
The 68-95-99.7 Rule is great when we’re dealing with whole-number
-scores. However, if the
-score is not a whole number, the Rule isn’t going to help us. Luckily, we can use technology to help us out. We’ll talk here about the built-in functions in Google Sheets, but other tools work similarly.
Let’s say we’re working with normally distributed data with mean 40 and standard deviation 7, and we want to know at what percentile a data value of 50 would fall. That corresponds to finding the proportion of the data that are less than 50. If we create our histogram and mark off whole-number multiples of the standard deviation like we did before, we’ll see why the 68-95-99.7 Rule isn’t going to help:
Figure
\(\PageIndex{33}\)
Since 50 doesn’t line up with one of our lines, the 68-95-99.7 Rule fails us. Looking back at Figure 8.51 and Figure 8.52, the best we can say is that 50 is between the 84th and 99.5th percentiles, but that’s a pretty wide range. Google Sheets has a function that can help; it’s called NORM.DIST. Here’s how to use it:
Click in an empty cell in your worksheet.
Type “=NORM.DIST(“
Inside the parentheses, we must enter a list of four things, separated by commas: the data value, the mean, the standard deviation, and the word “TRUE”. These have to be entered in this order!
Close the parentheses, and hit Enter. The result is then displayed in the cell; convert it to a percent to get the percentile.
So, for our example, we should type “=NORM.DIST(50, 40, 7, TRUE)” into an empty cell, and hit Enter. The result is 0.9234362745; converting to a percent and rounding, we can conclude that 50 is at the 92nd percentile. Let’s walk through a few more examples.
Example \(\PageIndex{9}\): Using Google Sheets to Find Percentiles
Suppose we have data that are normally distributed with mean 28 and standard deviation 4. At what percentile do each of the following data values fall?
30
23
35
Answer
By entering “=NORM.DIST(30, 28, 4, TRUE)” we find that 30 is at the 69th percentile.
By entering “=NORM.DIST(23, 28, 4, TRUE)” we find that 23 is at the 11th percentile.
By entering “=NORM.DIST(35, 28, 4, TRUE)” we find that 35 is at the 96th percentile.
Your Turn \(\PageIndex{9}\)
Suppose you have data that are normally distributed with mean 20 and standard deviation 6. Determine at what percentiles these data values fall: 25, 12, and 31.
Google Sheets can also help us go the other direction: If we want to find the data value that corresponds to a given percentile, we can use the NORM.INV function. For example, if we have normally distributed data with mean 150 and standard deviation 25, we can find the data value at the 30th percentile as follows:
Click on an empty cell in your worksheet.
Type “=NORM.INV(“
Inside the parentheses, we’ll enter a list of three numbers, separated by commas: the percentile in question expressed as a decimal, the mean, and the standard deviation. These must be entered in this order!
Close the parentheses and hit Enter. The desired data value will be in the cell!
In our example, we want the 30th percentile; converting 30% to a decimal gives us 0.3. So, we’ll type “=NORM.INV(0.3, 150, 25)” to get<PHONE_NUMBER>; let’s round that off to 137.
Example \(\PageIndex{10}\): Using Google Sheets to Find the Data Value Corresponding to a Percentile
Suppose we have data that are normally distributed with mean 47 and standard deviation 9. Find the data values (rounded to the nearest tenth) corresponding to these percentiles:
75th (that’s the third quartile)
12th
90th
Answer
By entering “=NORM.INV(0.75, 47, 9)” we find that 53.1 is at the 75th percentile.
By entering “=NORM.INV(0.12, 47, 9)” we find that 36.4 is at the 12th percentile.
By entering “=NORM.INV(0.9, 47, 9)” we find that 58.5 is at the 90th percentile.
Your Turn \(\PageIndex{10}\)
Suppose we have data that are normally distributed with mean 5 and standard deviation 1.6. Identify which data values (rounded to the nearest tenth) correspond to these percentiles: 25th (the first quartile), 80th, and 10th.
Check Your Understanding
For each of these problems, assume we’re working with normally distributed data with mean 100 and standard deviation 12.
What percentage of the data falls between 76 and 124? Use the 68-95-99.7 Rule.
What percentage of the data falls between 100 and 112? Use the 68-95-99.7 Rule.
At what percentile does 112 fall? Use the 68-95-99.7 Rule.
What’s the \(z\)-score of the data value 107? Round to three decimal places.
What data value’s \(z\)-score is –2.4?
At what percentile does 107 fall? Use Google Sheets (or another technology).
What data value is at the 90th percentile? Use Google Sheets (or another technology), and round to the nearest hundredth.


1.2: Drug Sources, Forms, and Names
By the end of this section, you should be able to:
- 1.2.1 Discuss drug sources and forms.
- 1.2.2 Explain drug standards.
- 1.2.3 Discuss the U.S. Food and Drug Administration’s drug approval process.
- 1.2.4 Define the chemical name, generic name, and trade name of drugs.
- 1.2.5 Explain the difference between a drug’s generic and brand name equivalents.
- 1.2.6 Differentiate between prescription and over-the-counter drugs.
- 1.2.7 Compare and contrast traditional drugs, biologics, and alternative and complementary drug therapies.
Drug Sources and Forms
Drugs are substances or compounds that prevent, treat, diagnose, or cure various conditions or diseases. As mentioned previously, drugs come from a variety of resources—plants, animal products, and inorganic substances. Ideally, these chemicals have desirable therapeutic effects without harmful properties, although many derivatives may have poisonous effects, depending upon the dosage used. Some plant-based products in use today include digitalis from the foxglove plant, vincristine from the periwinkle, and morphine from the poppy plant. The Amazon Basin is home to countless numbers of plants with medicinal properties. Though not in use today, one of the first paralytic agents used in anesthesia was curare, made from a vine known as Chondrodendron tomentosum , found near the Amazon River in South America. The Indigenous population in the area placed a curare mixture on the tip of blow darts and used it for hunting (see Figure 1.3). The prey is paralyzed once curare enters the bloodstream; however, the meat is safe to eat because it is not toxic when eaten and digested.
Vy states that she has been taking 1000 mg of acetaminophen every 4 hours per day for the last 48 hours. What is the priority nursing intervention for the client?
- Draw liver enzymes immediately.
- Educate the client about dosage recommendations for acetaminophen.
- Notify the health care provider to recommend initiation of hydrocodone.
- Immediately assess the client’s bowel function.
- Answer
-
Educate the client about dosage recommendations for acetaminophen.
The nurse is discussing the use of over-the-counter medications with Vy. What advantages of using over-the-counter drugs will the nurse discuss?
- Over-the-counter drugs do not need to be reported to the provider.
- Insurance companies will reimburse the cost of over-the-counter medications.
- Minor ailments can be treated by the client rather than the prescriber.
- Over-the-counter drugs are always safe.
- Answer
-
Minor ailments can be treated by the client rather than the prescriber.
Drug Standards
The FDA is the government agency responsible for the regulation of the development, production, and sale of drugs. The FDA was given much closer control over the production of drugs after the drug thalidomide was prescribed to pregnant clients for the treatment of morning sickness and for sedation in the 1950s and 1960s. This drug was found to be highly teratogenic to fetuses and caused many fetal malformations, especially limb defects. The Kefauver-Harris Amendments in 1962 required drug manufacturers to establish the efficacy of drugs and gave the FDA more control over the testing of drugs before they were placed on the market.
The United States Pharmacopeia National Formulary (USP-NF) has a rich history, dating back to the inaugural publication of the United States Pharmacopeia in 1820. In 1888, the National Formulary (NF) joined its ranks. Then, in 1975, these two entities merged to form the formidable USP-NF. This institution operates as an independent, nonprofit organization dedicated to establishing vital standards for compounding medications, biologics, drug development, and manufacturing, both within the United States and globally. It produces the only official book of drug standards in the United States annually, and drugs referenced in this book have met very high standards for quality, strength or potency, and purity (U.S. Pharmacopeia, n.d.). The USP-NF is a nongovernment organization and is not associated with the FDA; however, the standards that the USP-NF puts forth are enforced by the FDA as the official standards for the production and quality control of drugs and dietary supplements in the United States. Complementary medications and supplements are not required to meet these same standards, though some do, and they carry a USP seal guaranteeing safe manufacturing and quality. The Canadian Food and Drugs Act also recognizes the USP-NF as a reliable authority of drug standards in Canada for health care providers.
There are many reliable sources of drug information for health care providers, including the American Society of Health System Pharmacists’ AHFS Drug Information book and Drug Facts and Comparisons. Many online resources are available to nurses, such as Medscape, Skyscape , DailyMed , and UpToDate . Applications can be downloaded to personal electronic devices or devices in the clinical setting (though not all are free). Often hospitals/health care systems and clinics have institutional access to these resources. It is helpful for the nurse to be able to access a wide variety of resources. A useful and free app for herbal supplements is About Herbs , which is produced by Memorial Sloan Kettering Cancer Institute. It is particularly helpful in the discussion of the mechanisms of action, herb–drug interactions, and adverse effects.
Drug Approval Process
The Center for Drug Evaluation and Research (CDER) is a branch of the FDA that evaluates new drugs before they can be sold in the United States. It provides health care providers and consumers with the information needed to use drugs appropriately. One of the tasks of CDER is to ensure that both brand-name and generic drugs work as they should (FDA, 2022b). Before any drug is sold in the United States, it must be tested. Once a chemical that may have therapeutic effects is isolated, it will undergo scientific tests and clinical trials to prove its efficacy and safety. Drugs must pass through several stages of development before receiving approval from the FDA to be marketed to consumers. Table 1.2 briefly describes the process of obtaining FDA approval for a new drug.
| Stage of Development | Description |
|---|---|
| Preclinical trials | Drugs are tested on animal subjects to evaluate the compound in living tissues and to evaluate for adverse effects. |
| Phase I studies | A very small sample of human volunteers is used to test the drugs. Usually, the individuals are healthy subjects. |
| Phase II studies | Clinical investigators test the drug on clients with the disease that the drug has been developed to treat. Subjects are monitored very closely to evaluate the intended effects and for adverse reactions. |
| Phase III studies | This is a large-scale clinical trial. Prescribers assist in observing the client taking the drug. The goal of this phase is to gather data about the effectiveness and safety of the drug. |
| FDA approval | Once drugs make it through Phase III studies, they are evaluated by the FDA following the submission of a New Drug Application. If the FDA approves the drug, it can then be marketed. |
|
Phase IV studies
(post-marketing surveillance) |
Continual evaluations of drugs following approval by the FDA |
Some estimate the cost of developing just one new drug as ranging from less than $1 billion to over $2 billion (Wouters et al., 2020). The development of new drugs requires 10–15 years before the testing and drug studies are complete. Thousands of compounds are tested yearly, but only a few make it to clinical trials. Once a drug enters clinical trials, only a few are approved. CDER does not perform the clinical trials—this is up to the drug company; however, CDER reviews the pharmaceutical company’s data and the proposed drug labeling. At the heart of the approval process is establishing the health benefits of the drug and its safety profile. Does the benefit of the drug outweigh its risk?
There also are limitations to testing new drugs. Historically, there has been limited testing of drugs in the populations of females (particularly those of childbearing age), children (anyone under the age of 18 years old), and people of color (POC) . This has recently changed, but before 2000, minimal testing was performed on females. No clinical trials were allowed for females of childbearing age, even if they were not pregnant and were taking effective birth control. This greatly limited knowledge about how females would respond to many drugs. This also means that there is limited data about drug safety during pregnancy.
Children were also excluded from clinical trials in the past, though some exceptions have been made more recently. According to the FDA (2016), only about 20% of drugs are approved for pediatric use. For that reason, physicians have had to prescribe drugs “off-label” for children. The problem is that well-controlled clinical trials for pediatric dosing have not been established for many drugs, meaning there is no safety data for most drugs. The reasons for this lack of data are somewhat surprising. Some believe that pharmaceutical companies saw little profit in medications for children. Finding adequate numbers of children for robust testing could be more of a challenge than with adults, especially in trials where blood would need to be drawn.
There are also ethical reasons to exclude children from trials, especially because children are unable to give consent. According to the FDA (2016, para. 7), “Parents are involved in the decision to enroll children in a study, and children ages 7 or older can ‘assent’ or ‘dissent,’ meaning they can agree or disagree to participate in a study.” For more information about children’s assent and parental permission, review this article by the National Cancer Institute . More testing has been conducted in the last decade, and the information coming out of those studies has shown much more accurate methods for dosing and prescribing. The FDA web page on drug research and children has more information.
Link to Learning
In this video from the National Heart, Lung, and Blood Institute, pediatric researchers, doctors, and nurses explain why children should be included in clinical drug trials and answer common questions from parents and caregivers.
Even as late as 2019, only 15%–19% of participants in U.S. clinical drug trials were Black. When broken down further, only 3% of participants in clinical trials for cardiovascular disease were Black males (6% were Black females), and less than 5% of oncology trial participants were Black males (2% were Black females) (Whyte, 2022). When specific populations are under-represented in a clinical trial, the efficacy and safety are unknown for that particular subset. The efficacy and safety of some drugs may differ for various ethnicities and genetic backgrounds (Clark et al., 2019). Due to a variety of barriers, racial and ethnic minorities are often underrepresented in clinical trials (FDA, 2022a). When clinical trials test therapies only within a homogenous group, the findings are likely to be skewed. One of the problems with this is that minority groups may have less benefit from those therapies. Some of the barriers to increasing minority involvement in clinical trials include a lack of understanding of the value of the process; mistrust of research; lack of information, time, and resources; and a lack of knowledge about the existence of various trials (Clark et al., 2019). Many differences in health outcomes have been documented based on race and ethnicity, underscoring the importance of including a variety of racial and ethnic groups in clinical trials.
Trending Today
Off-label prescription drug use pertains to the utilization of a medication in ways that deviate from the specifications provided on the drug’s label or within its FDA-approved package insert. Such usage encompasses employing the medication for divergent medical conditions, adjusting dosages, targeting varying client groups, or employing alternative routes of administration, all of which may contrast with the medication’s initial FDA approval. For example, after the COVID-19 pandemic started, a research institute focused its efforts on repurposing existing drugs (see the Wyss Institute website and the video above).
The safety of off-label drug use varies based on factors such as the specific medication and condition, the health care provider’s judgment and knowledge, and the available evidence. Providers should weigh potential benefits against risks (as discussed in the Wall Street Journal video above) because off-label use may be valuable when approved alternatives are lacking. This article provides 2023 statistics on the drug classes in which off-label use is typical.
When administering off-label drugs, nurses should ensure informed consent from the client or their representative, discussing potential risks and benefits of the proposed medication. The nurse should also collaborate with the health care team for guidance and to determine the appropriateness of the drug. Other duties of the nurse include documenting the rationale for the use of the drug, educating the client on its proper use, monitoring the client closely for unexpected effects, and staying updated on the latest research. Above all, it is important for the nurse to uphold ethical principles and keep the client’s well-being at the forefront.
Canadian Drug Regulation
The United States’ and Canada’s drug laws have evolved in a similar manner. Any drug manufacturer must provide scientific evidence of the drug’s safety, efficacy, and quality to Health Canada before the sale of that product is authorized. The federal review process by Health Canada was empowered by the Food and Drugs Act of 1927 as well as additional regulations (in 1953, 1954, and 1979) intended to protect consumers from risks associated with the production and sale of drugs, cosmetics, food, and medical devices (Health Canada, n.d.). It regulates the manufacturing, packaging, labeling, storage, and sale of food and drugs in Canada. It also determines whether a drug is a prescription or nonprescription medication. Canada requires prescriptions for narcotics and strict guidelines for record-keeping for prescribing and dispensing those drugs. Drug use in Canada is regulated and enforced by many different agencies, including Health Canada, the Royal Canadian Mounted Police, and agencies within various provinces.
Although the schedule of drugs is different from that of the United States (see Drug Classifications and Prototypes for U.S. drug schedules), Canada has tried to align the schedules throughout each province so that the sale of medications is consistent throughout the country. The governing body for Canadian national drug schedules is the National Association of Pharmacy Regulatory Authorities (NAPRA) , though each province does regulate how drugs are sold or dispensed. In Canada, drugs are assigned to one of four categories (Drug & Alcohol Testing Association of Canada [DATAC], 2017; National Association of Pharmacy Regulatory Authorities, 2023):
- Schedule I: All prescription drugs. These drugs must be provided to the consumer by a pharmacist through a controlled, regulated environment. Drugs in this category include heroin, cocaine, morphine, and methadone.
- Schedule II: Restricted-access nonprescription drugs. These drugs may require professional intervention from a pharmacist when sold to the consumer. Must be stored with no public access or chance for the public to choose or select the drug (in other words, “behind the counter”). Schedule II drugs include insulin, pseudoephedrine, and sublingual nitroglycerin.
- Schedule III: Available without a prescription and sold on store shelves under the supervision of a pharmacist. Examples in this category are ibuprofen or naproxen.
- Unscheduled: Can be sold without supervision from any retail outlet. Emergency contraception, such as norgestrel, is considered an unscheduled drug.
Drug Counterfeiting
The United States has one of the world’s safest drug supplies, partly due to the FDA and USP-NF. Unfortunately, counterfeit drugs threaten that safety. Counterfeit drugs are products that are illegally manufactured or mislabeled with regard to their identity or source so that they appear to be a genuine product. They are fake drugs and may harm the recipient's health. Illegal online sales expose consumers to potential counterfeit drugs. Counterfeit drugs are not the same thing as generic medications. As mentioned earlier in this chapter, a generic drug is manufactured with the intent that it is identical in the active chemical ingredients, safety, strength, quality, dosage form, and intended use as its brand name counterpart (FDA, 2021a). These are drugs approved by the FDA. Counterfeit drugs are not.
Consumers can protect themselves by buying medications from state-licensed pharmacies in the United States. If using an online pharmacy, the consumer should check for the online pharmacy’s license through the state Board of Pharmacy. If it is not listed, that pharmacy should not be used. Reputable online pharmacies should have a seal of approval from the Verified Internet Pharmacy Practice Site (VIPPS) . An online pharmacy should always require a health care provider’s prescription and have a physical address and phone number in the United States. Another strategy for the consumer is to be alert to changes or variations in the packaging of medications—for example, the color or lettering might be different. Consumers should be alert to any unusual taste or side effects from the drug and report it immediately. Concerns about potential counterfeit drugs should be reported to the FDA through its MedWatch website or by calling 1-800-FDA-1088. The 2013 Drug Supply Chain Security Act was passed as an initiative to combat the production and use of counterfeit drugs. The FDA’s Office of Criminal Investigations investigates counterfeit drugs, their producers, and their sellers and attempts to bring them to justice.


10.2: Solution of a Symmetrical Electrolyte
Let us consider properties of an electrolyte solute as a whole. The simplest case is that of a binary solution in which the solute is a symmetrical strong electrolyte—a substance whose formula unit has one cation and one anion that dissociate completely. This condition will be indicated by \(\nu = 2\), where \(\nu\) is the number of ions per formula unit. In an aqueous solution, the solute with \(\nu\) equal to 2 might be a 1:1 salt such as NaCl, a 2:2 salt such as MgSO\(_4\), or a strong monoprotic acid such as HCl.
In this binary solution, the chemical potential of the solute as a whole is defined in the usual way as the partial molar Gibbs energy \begin{equation} \mu\B \defn \Pd{G}{n\B}{T,p,n\A} \tag{10.2.1} \end{equation} and is a function of \(T\), \(p\), and the solute molality \(m\B\). Although \(\mu\B\) under given conditions must in principle have a definite value, we are not able to actually evaluate it because we have no way to measure precisely the energy brought into the system by the solute. This energy contributes to the internal energy and thus to \(G\). We can, however, evaluate the differences \(\mu\B - \mu\mbB\rf\) and \(\mu\B - \mu\mbB\st\).
We can write the additivity rule (Eq. 9.2.25) for \(G\) as either \begin{equation} G = n\A\mu\A + n\B\mu\B \tag{10.2.2} \end{equation} or \begin{equation} G = n\A\mu\A + n_{+}\mu_{+}+n_{-}\mu_{-} \tag{10.2.3} \end{equation} A comparison of these equations for a symmetrical electrolyte (\(n\B = n_+ = n_-\)) gives us the relation \begin{gather} \s{ \mu\B = \mu_{+} + \mu_{-} } \tag{10.2.4} \cond{(\(\nu{=}2\))} \end{gather} We see that the solute chemical potential in this case is the sum of the single-ion chemical potentials.
The solution is a phase of electric potential \(\phi\). From Eqs. 10.1.4 and 10.1.5, the sum \(\mu_{+}+\mu_{-}\) appearing in Eq. 10.2.4 is \begin{equation} \mu_+(\phi) + \mu_-(\phi)=\mu_+(0) + \mu_-(0) + (z_+ + z_-)F\phi \tag{10.2.5} \end{equation} For the symmetrical electrolyte, the sum \((z_+ + z_-)\) is zero, so that \(\mu\B\) is equal to \(\mu_+(0) + \mu_-(0)\). We substitute the expressions of Eq. 10.1.10, use the relation \(\mu\mbB\rf=\mu_+\rf+\mu_-\rf\) with reference states at \(\phi{=}0\), set the ion molalities \(m_+\) and \(m_-\) equal to \(m\B\), and obtain \begin{gather} \s{ \mu\B=\mu\mbB\rf +RT\ln\left[\g_+\g_-\left(\frac{m\B}{m\st}\right)^2\right] } \tag{10.2.6} \cond{(\(\nu{=}2\))} \end{gather} The important feature of this relation is the appearance of the second power of \(m\B/m\st\), instead of the first power as in the case of a nonelectrolyte. Also note that \(\mu\B\) does not depend on \(\phi\), unlike \(\mu_+\) and \(\mu_-\).
Although we cannot evaluate \(\g_+\) or \(\g_-\) individually, we can evaluate the product \(\g_+\g_-\). This product is the square of the mean ionic activity coefficient \(\g_{\pm}\), defined for a symmetrical electrolyte by \begin{gather} \s{ \g_{\pm} \defn \sqrt{\g_{+}\g_{-}} } \tag{10.2.7} \cond{(\(\nu{=}2\))} \end{gather} With this definition, Eq. 10.2.6 becomes \begin{gather} \s{ \mu\B=\mu\mbB\rf +RT\ln\left[\left(\g_{\pm}\right)^2 \left(\frac{m\B}{m\st}\right)^2\right] } \tag{10.2.8} \cond{(\(\nu{=}2\))} \end{gather} Since it is possible to determine the value of \(\mu\B-\mu\mbB\rf\) for a solution of known molality, \(\g_{\pm}\) is a measurable quantity.
If the electrolyte (e.g., HCl) is sufficiently volatile, its mean ionic activity coefficient in a solution can be evaluated from partial pressure measurements of an equilibrated gas phase. Section 10.6 will describe a general method by which \(\g_{\pm}\) can be found from osmotic coefficients. Section 14.5 describes how, in favorable cases, it is possible to evaluate \(\g_{\pm}\) from the equilibrium cell potential of a galvanic cell.
The activity \(a\mbB\) of a solute substance on a molality basis is defined by Eq. 9.7.8: \begin{equation} \mu\B = \mu\mbB\st + RT\ln a\mbB \tag{10.2.9} \end{equation} Here \(\mu\mbB\st\) is the chemical potential of the solute in its standard state, which is the solute reference state at the standard pressure. By equating the expressions for \(\mu\B\) given by Eqs. 10.2.8 and 10.2.9 and solving for the activity, we obtain \begin{gather} \s{ a\mbB = \G\mbB \left(\g_{\pm}\right)^2 \left(\frac{m\B}{m\st}\right)^2 } \tag{10.2.10} \cond{(\(\nu{=}2\))} \end{gather} where \(\G\mbB\) is the pressure factor defined by \begin{equation} \G\mbB \defn \exp\left(\frac{\mu\mbB\rf-\mu\mbB\st}{RT}\right) \tag{10.2.11} \end{equation} We can use the appropriate expression in Table 9.6 to evaluate \(\G\mbB\) at an arbitrary pressure \(p'\): \begin{equation} \G\mbB(p')=\exp\left(\int_{p\st}^{p'}\frac{V\B^{\infty}}{RT}\difp\right) \approx \exp\left[\frac{V\B^{\infty}(p'-p\st)}{RT}\right] \tag{10.2.12} \end{equation} The value of \(\G\mbB\) is \(1\) at the standard pressure, and close to \(1\) at any reasonably low pressure. For this reason it is common to see Eq. 10.2.10 written as \(a\mbB=\g_{\pm}^2(m\B/m\st)^2\), with \(\G\mbB\) omitted.
Equation 10.2.10 predicts that the activity of HCl in aqueous solutions is proportional, in the limit of infinite dilution, to the square of the HCl molality. In contrast, the activity of a non electrolyte solute is proportional to the first power of the molality in this limit. This predicted behavior of aqueous HCl is consistent with the data plotted in Fig. 10.1, and is confirmed by the data for dilute HCl solutions shown in Fig. 10.2(a). The dashed line in Fig. 10.2(a) is the extrapolation of the ideal-dilute behavior given by \(a\mbB=(m\B/m\st)^2\). The extension of this line to \(m\B = m\st\) establishes the hypothetical solute reference state based on molality, indicated by a filled circle in Fig. 10.2(b). (Since the data are for solutions at the standard pressure of \(1\br\), the solute reference state shown in the figure is also the solute standard state.)
The solid curve of Fig. 10.2(c) shows how the mean ionic activity coefficient of HCl varies with molality in approximately the same range of molalities as the data shown in Fig. 10.2(b). In the limit of infinite dilution, \(\g_{\pm}\) approaches unity. The slope of the curve approaches \(-\infty\) in this limit, quite unlike the behavior described in Sec. 9.5.4 for the activity coefficient of a nonelectrolyte solute.
For a symmetrical strong electrolyte, \(\g_{\pm}\) is the geometric average of the single-ion activity coefficients \(\g_+\) and \(\g_-\). We have no way of evaluating \(\g_+\) or \(\g_-\) individually, even if we know the value of \(\g_{\pm}\). For instance, we cannot assume that \(\g_+\) and \(\g_-\) are equal.


2.11: Why It Matters- Writing in Business
“Oh my gosh, what a disaster!” Dennis thought. Dennis had been placed in charge of booking a speaker for his office’s diversity event, and the major speaker from Diversity NOW hadn’t shown up. He was now scrambling to figure out what may have gone wrong, all under the watchful eye of his boss, Carmen.
As Dennis checked his email, he found that he had, in fact, reached out to Rennata, the speaker he had booked:
Date: 4/2/18
To: Rennata Johnson, CEO of Diversity NOW <<EMAIL_ADDRESS>>
From: Dennis Hoffman <<EMAIL_ADDRESS>>
Re: Diversity Day
I am Dennis Hoffman and my boss, Carmen Jacobson, asked me to get a hold of you about Diversity day later in this month. You may or may not know that our company is working on a proactive stance about diversity in the workplace. Last year we put on sessions and skits ourselves. Several employees enjoyed the day but did not seem to fully appreciate the implications for the work day. We are so thankful that you agreed to help out since you are a formal outsider. We’re going to set aside the entire day of April 23. The first session starts at 9 a.m. There will be lunch and a the late afternoon session goes only until 3:00 p.m. The brochures need to be published by one week prior to that date. See you then!
Dennis
Dennis suddenly realizes that Rennata never sent a response back—confirming or otherwise. He thinks to himself, “Yes, I probably should have remembered to check to see if Rennata had sent the title for her talk back in time. I did get a bit busy and forget that. But how could Carmen think the rest of this was my fault?”
In this module, we’ll discuss the importance of writing—and discuss appropriate ways to write—in business.
Look back at Dennis’s email. Do you see areas where he could improve this email?
Let’s dive in and learn the skills you need to ensure a much better outcome in your work.
Contributors and Attributions
- Why It Matters: Writing in Business. Authored by : Susan Kendall. Provided by : Lumen Learning. License : CC BY: Attribution


8.4: Supportive versus Defensive Communication
Six Ways to Understand Communication Climate
Our natural instinct when we feel threatened is to become defensive. As a defensive communicator, we focus on protecting ourselves and our interests, which is not necessarily a bad thing as we must learn to stand up for ourselves. Unfortunately, defensiveness is usually a negative cycle that creates defensiveness in others and causing conflicts to escalate. For example, defensive behaviors, such as rolling the eyes, not listening, making excuses, or blaming others can cause your communication partner to argue back louder, walk away, blame you back, and gunnysack (a non-productive conflict tactic where one saves up or “gunnysacks” all their complaints until they burst with anger, spilling all their grievances at once) all sorts of other issues to “make a mountain out of a molehill.” And we have all been in the position before where the more defensive we become, the less we are able to communicate effectively.
Conversely, supportive climates create more calm and productive communication outcomes. This allows for communicators to better focus on the intent and meanings of messages. In 1965, psychologist Jack Gibb came up with six pairs of supportive and defensive behaviors to help provide a better context for learning about and understanding communication climate: description versus evaluation, collaboration versus control, straightforwardness versus manipulation, empathy versus indifference, equality versus superiority, and flexibility versus certainty. Here, we examine each pair and consider its potential behaviors and outcomes.
Description versus Evaluation
Description
- Description: Neutral facts that avoid any “loaded” words or judgments.
- Example: “I feel left out when you guys go to the mall without me.”
- Possible interpretation: The recipient knows exactly how you feel about what is bothering you. Using the I-message doesn’t place blame and communicates an openness for calm discussion.
Evaluation
- Description: Statements containing a tone of accusation, blame, and/or judgment.
- Example: “You guys always abandon me and leave me out when you go to the mall.”
- Possible interpretation: Recipient feels attacked and judged for something that may have been unintended and misinterpreted.
Collaboration versus Control
Collaboration
- Description: The parties involved are working with each other toward a win-win situation. Everyone’s voice and ideas are just as important as the next person’s.
- Example: “Let’s go around and share what has worked for you and what has not worked for you.”
- Possible interpretation: Everyone involved feels included, respected, and productive.
Control
- Description: Speaker conveys a “know it all” attitude showing little or no interest in the receiver’s needs and ideas.
- Example: “I’m right. You’re wrong. We do it my way, or we don’t do it at all!”
- Possible interpretation: Recipients may feel hostile, competitive, and disrespected toward the speaker which may result in reluctance and uncooperativeness.
Straightforwardness versus Manipulation
Straightforwardness
- Description: Direct, candid, unambiguous, open, and honest
- Example: “You didn’t get a raise this quarter, because while you made more sales, the volume of those sales has been down.”
- Possible interpretation: The recipient may still be disappointed but knows exactly how and why the raise did not come through.
Manipulation
- Description: To exploit, maneuver, or mastermind with hidden intentions
- Example: “If you put in more hours and effort like John, you might have seen a raise this quarter.”
- Possible interpretation: Recipients may feel judged that John did a better job and that his/her efforts were not appreciated. Recipients may also feel defeated and unmotivated.
Empathy versus Indifference
Empathy
- Description: Walking a mile in the other person’s shoes and trying to relate to and support the other person
- Example: “I understand you’re going through a rough time. I hope that extending the deadline for you will help relieve some of the stress and pressure you must be feeling.”
- Possible interpretation: Recipient feels compassion, understanding, and even relief.
Indifference
- Description: Apathetic, detached, and aloof; general lack of concern for the other person.
- Example: “Everyone’s got problems! If you can’t meet the deadline, you’ll just have to pay the penalty.”
- Possible interpretation: Recipient feels unimportant and insignificant.
Equality versus Superiority
Equality
- Description: A sense of fairness, justness, and impartiality; everyone has the same chance.
- Example: “I remember struggling when I first started, too. It’s going to take some time, but let me help you.”
- Possible interpretation: Recipients may feel validated, respected, and capable.
Superiority
- Description: Communicating a sense of dominance; having an upper-hand
- Example: “ I’ve shown you how to do this a million times! Move over! Let me finish it!”
- Possible interpretation: Recipients may feel inept, inadequate, defensive, and angry.
Flexibility versus Certainty
Flexibility
- Description: Open-minded and show a willingness to adapt to something better
- Example: “I’d love to learn how we can use this new technology to work smarter, not harder.”
- Possible interpretation: Recipients may feel encouraged to investigate, share, and try new things.
Certainty
- Description: Overconfidence that “I’m right. You’re wrong.” No other input is needed.
- Example: “I’ve done this a million times. This is the only way to fix it!”
- Possible interpretation: Recipient feels unwelcome, unvalued, and unwilling to put themselves out there.
Directions: Randomly choose a scenario and use one of Gibb’s supportive communication strategies discussed in section 8.2 to reframe the situation.
Scenario 1
- Person 1: "Why do I have to nag you every time to unload the dishwasher?"
- Person 2: "Why do you have to nag me when you know I’ll eventually get to it?"
Scenario 2
- Person 1: "Why can’t you ever get ready on time when we go out to dinner with my parents?"
- Person 2: "Well, last time we went out with my family I had to wait for you to get home from work and we ended up stuck in traffic for an extra hour!"
Scenario 3
- Person 1: "Why don’t you ever clean your room like I ask you?"
- Person 2: "I know I’m supposed to keep my room clean, but I work two jobs and I just don’t have the time or energy to keep my room clean."
Scenario 4
- Person 1: "You got home so late we missed our dinner reservation! Why didn’t you call?"
- Person 2: "I know, but I got caught up at work with a big project and forgot."
Scenario 5
- Person 1: "The computer has been running super slow since you last used it!"

Scenario 6
- Person 1: "When can we leave?"
- Person 2: "I just want to stay until after the award presentation."
- Person 1: "I really want to leave now!"
- Person 2:" Come on! Just wait until after the awards!"
Discussion Questions
- What are some adjectives that come to mind when you hear the word defensiveness ? What are some adjectives that come to mind when you hear the word supportiveness ?
- Were there supportive strategies you found easier to use than others? If so, what were they and why were they easier for you to use?
- Share a scenario you think would have benefited from one of Gibb’s supportive communication strategies.


0.2: Economic Justice is Valuing Work!
Economic Justice
Productive work creates and drives economies. Economies are about the efficient use of trade, capital, natural and human resources. The term ‘economic justice’ will be defined and described centering on the discussion of the history of work in the US and its association with worker safety.
Investopedia defines ‘economic justice’ as a component of social justice and welfare economics. It is a set of moral and ethical principles for building economic institutions, where the ultimate goal is to create an opportunity for each person to establish a sufficient material foundation upon which to have a dignified, productive, and creative life.
Much of the prior discussion has focused on human experiences and functions of society norms. The history of work in the US also includes government, commercial, and private institutions, regulatory and organizational structures. Most forms of government exists to provide order and to promote the common good.
Recognizing commerce and commercial enterprise garnered most of the attention of lawmakers who afforded the most protection to business and industry, advocates for labor began making waves immediately after the American Civil War in 1864. As referenced from historical text, government oversight of labor practices was initiated…when William Sylvis, the most important labor leader of his day, advocated for the creation of a Department of Labor (DOL). He protested that existing government departments threw their protective arms around every enterprise fostering wealth, while no department had as its "sole object the care and protection of labor." He and his followers petitioned President Andrew Johnson for a Secretary of Labor, chosen from the ranks of workingmen, to be labor's voice in the Cabinet.
It took nearly 50 years before an American President, Andrew Taft, signed a bill creating a cabinet level Department of Labor in 1913. The DOL established a bureau of labor statistics (BLS) to provide objective labor data not influenced by national politics. It took many more decades later for the BLS to not be influenced by social norms and today there is relative assurance that labor data is unbiased and accurate. The BLS provides important statistics on what is happening in workplaces such as pay and benefits, unemployment numbers, injury and illness data, employment projections, and productivity.
So, why is the DOL relevant to a discussion on economic justice and its ties to worker safety? In addition to the BLS, the DOL has under its arm a number of agencies such as the Occupational Safety and Health Administration (OSHA), the Mine Safety and Health Administration (MSHA), the Office of Workers Compensation Programs (OWCP), the Office of Labor-Management Standards (OLMS), the Wage and Hour Division (WHD), and the Employment and Training Administration (ETA). Of course OSHA and MSHA are centered on worker health and safety, however the other agencies collectively provide for ensuring a skilled and trained workforce, an ethically managed workforce, and a fairly compensated workforce.
Unions and Skilled Labor
The history of work in the US would not be complete without mention of unions. Many who will review this textbook may, if not currently union members, join a union sometime in the future. Skilled labor professionals are the primary beneficiaries of labor unions with the first documented US trade union formed in 1794, the Federal Society of Journeymen Cordwainers (Shoemakers). So what exactly is a union?
Investopedia states labor unions are associations of workers formed to protect workers' rights and advance their interests. Unions negotiate with employers through a process known as collective bargaining. The resulting union contract specifies workers’ pay, hours, benefits, and job health-and-safety policies.
Modern union contracts also add provisions for education, training, and certification requirements as well as stipulations for adverse or disciplinary actions and probationary waiting periods for full union benefits. The first trade unions were no different from most early American institutions in their exclusionary, discriminatory practices and denial of membership to ethnic minorities, African Americans, Asian Americans, and women. The first trade unions did not value all workers. Groups excluded from the membership of unions supporting only Anglo-Saxon and Protestant males, formed their own unions. During the civil rights years unions saw some relaxation of exclusionary practices however even at peak union membership in the 1980s, minorities and women were still underrepresented in some of the largest trade unions. There is still work to do in this area and promoted by active recruitment of women and minorities.
A number of the largest trade unions associated with skilled labor professionals include the International Brotherhood of Electrical Workers (IBEW), International Association of Bridge, Structural, Ornamental and Reinforcing Iron Workers Ironworkers (IW), International Association of Sheet Metal, Air, Rail and Transportation Workers (SMART), United Association of Journeymen and Apprentices of the Plumbing and Pipe Fitting Industry of the United States and Canada (UA), United Automobile, Aerospace and Agricultural Implement Workers of America International Union (UAW), United Brotherhood of Carpenters and Joiners of America (UBC), United Steelworkers (USW), International Association of Machinists and Aerospace Workers (IAM), and American Nurses Association (ANA).
Labor unions not trade oriented include the Amalgamated Transit Union (ATU), International Union of Operating Engineers (IUOE), Service Employees International Union (SEIU), International Brotherhood of Teamsters (IBT), and the Fraternal Order of Police (FOP) just to name a few. These are fully funded and influential unions often joining together on the national stage supporting legislation that implements labor policy favorable to workers.
Trade unions have played an important role in creating and elevating the economic status of the poor and working class to the middle class. Trade unions fight for safe working conditions and are responsible for helping to formulate many of the health and safety standards presented in this textbook. Unions continue to provide input and content for education requirements and training programs establishing trade certification and safety guidelines. Unions elevate the trades through apprenticeship programs supporting competitive living wages, bargaining for favorable retirement benefits, and advocating for superior medical benefits not often available to the average worker. Unions reduce wage inequality because they raise wages more for low- and middle-wage workers than for higher-wage workers, more for blue-collar than for white-collar workers, and more for workers who do not have a college degree. Strong unions set a pay standard that nonunion employers follow. Labor unions have been good for the trades and nonunion alike and crucial to economic justice despite exclusionary practices in the formative years.
Community College and Trade Schools
A persistent and equally effective means for economic justice is the community college and trade school. Community colleges and trade schools prepare workers with the education and training needed to enter the workforce as skilled laborers and excel in high paying and rewarding careers. The history of work in the US would not be complete without bringing attention to the alternative that low cost career and technical education programs have presented for improving living standards and skilled worker outcomes. Free, low cost, or subsidized training programs offer a good return on investment and are well suited for introducing students/workers to workplace safety standards and the methods for developing safe work practices and sustaining safe workplaces.
Fair pay, living wages, education and training, safe and healthy working conditions are core tenets upon which to correlate economic justice to worker safety. So how are healthy working conditions established? What are the criteria for worker safety and health? We will discuss in the next section how our desire to protect the environment addresses these questions.


8.5: Complications of Pregnancy and Delivery
Several complications may occur during pregnancy, labor, delivery, and postpartum.
Complications of Pregnancy
There are several complications that can occur during pregnancy that pose health risks to mother and child. Common complications include the following [1] :
- Gestational diabetes (jĕs-TĀ-shŏn-ăl dī-ă-BĒ-tĭs): High blood sugar levels that develop in a woman during pregnancy who has not been previously diagnosed with diabetes. After pregnancy, blood sugars typically return to normal although the mother has increased risk of developing type 2 diabetes mellitus later in life.
- Hyperemesis gravidarum (HĪ-pĕr-ĕm-Ē-sĭs GRAV-ĭ-dar-ŭm): Severe, persistent vomiting during pregnancy causing dehydration and weight loss. It is more severe than nausea associated with “morning sickness.”
- Preeclampsia (prē-ĕ-KLAMP-sē-ă): High blood pressure during pregnancy with additional signs like edema and protein in the urine. If not effectively treated, preeclampsia can evolve into eclampsia (ĕ-KLAMP-sē-ă), severe complication characterized by seizures and a risk of coma and death.
- Deep vein thrombosis (dēp vān throm-BOH-sĭs): The formation of a blood clot in a deep vein, most commonly in the legs.
- Postpartum hemorrhage (pōst-PÄR-tŭm HEM-ŏr-ij): Cumulative blood loss greater than 1,000 mL with signs and symptoms of hypovolemia within 24 hours of the birth process, regardless of the route of delivery. Hypovolemia (hī-pō-vō-LĒ-mē-ă) is a life-threatening condition where the plasma (liquid portion of the blood) is too low, resulting in the organs not receiving enough oxygenated blood flow. The most common cause of postpartum hemorrhage is lack of effective contraction of the uterus after childbirth. [2]
- Pregnancy-related death (PRĔG-năn-sē ri-LĀ-tĭd dĕth): The Centers for Disease Control and Prevention defines a pregnancy-related death as the death of a woman during pregnancy or within one year of the end of pregnancy from a pregnancy complication, a chain of events initiated by pregnancy, or the aggravation of an unrelated condition by the physiologic effects of pregnancy. Most of these pregnancy-related deaths are preventable. It is important for all women of reproductive age to adopt healthy lifestyles (e.g., maintain a healthy diet and weight, be physically active, quit all substance use, prevent injuries) and address any health problems before getting pregnant. A healthy pregnancy begins before conception and continues with prenatal care, along with early recognition and management of complications if they arise. [3]
Congenital Anomaly
A congenital anomaly (kŏn-JĔN-ĭ-tăl ă-NOM-ă-lē) is an abnormality in a baby that is present at birth. Many congenital anomalies are caused by teratogens (tĕr-AT-ŏ-jĕnz), substances toxic to a developing fetus. For example, excessive alcohol intake by the mother can cause fetal alcohol spectrum disorder. Maternal diseases during pregnancy can also cause a birth defect or a chronic health problem. Some diseases that are known to potentially have an adverse effect on the fetus include diabetes, cytomegalovirus, toxoplasmosis, rubella, varicella, hypothyroidism, and infections caused by streptococcus B . For example, if an unvaccinated mother contracts rubella during the first three months of pregnancy, damage can occur to the baby’s eyes, ears, heart, or brain. [4]
Fetal Alcohol Spectrum Disorders
One of the most commonly used teratogens is alcohol. Because half of all pregnancies in the United States are unplanned, it is recommended that women of child-bearing age take great caution against drinking alcohol when not using birth control and when pregnant. Alcohol consumption, particularly during the second month of prenatal development, but at any point during pregnancy, may lead to neurocognitive and behavioral difficulties that can last a lifetime. [5]
There is no acceptable safe limit for alcohol use during pregnancy, but binge drinking (five or more drinks on a single occasion) or having seven or more drinks during a single week places a child at particularly high risk. In extreme cases, alcohol consumption can lead to fetal death, but more frequently it can result in fetal alcohol spectrum disorders (FĒ-tăl AL-kŏ-hŏl SPEK-trŭm dĭs-ŎR-dĕrz) (FASD) . This terminology is now used when looking at the effects of exposure and replaces the term fetal alcohol syndrome. It is preferred because it recognizes that symptoms occur on a spectrum and that all individuals do not have the same characteristics. Children with FASD share certain physical features such as flattened noses, small eye openings, small heads, intellectual developmental delays, and behavioral problems. See Figure 8.9 [6] for an image of a child with FASD. [7]
Premature Labor and Premature Infant
Preterm labor (PRĒ-tûrm LĀ-bŏr) is defined as labor occurring between 20 and 37 weeks of gestation. There are many potential causes of preterm labor, such as stress, infection, placental abruption, placenta previa, substance use, history of preterm birth or abortion, inadequate prenatal care, smoking, maternal age <18 or >40, poor nutrition, low body mass index, fetal congenital anomaly, fetal growth restriction, oligohydramnios, polyhydramnios, vaginal bleeding, premature preterm rupture of membranes (prē-mă-CHŪR prē-tûrm RŬP-chĕr ŏv MEM-brānz) (PPROM) , and other environmental factors. [8]
The main concern with premature labor is the prematurity of the infant, which can cause respiratory distress and impaired neurological development. A premature infant (prē-mă-CHŪR IN-fănt) refers to an infant who is born before completing 37 weeks of gestation. Treatment of premature labor is based on the gestational age at which the mother presents to the hospital and may include medications to stop labor from progressing, such as magnesium sulfate. [9]
Cerclage (sĕr-KLĂZH) is a procedure typically performed during the second semester of pregnancy to prevent preterm labor and miscarriage. During the procedure, the cervix is sewed shut with sutures to prevent dilation and premature delivery. It is performed in women whose cervix isn’t strong enough to stay closed as the pregnancy grows. The suture is removed when the fetus is full-term, which allows vaginal delivery to proceed. [10]
Miscarriage and Stillbirth
Pregnancy loss is experienced in over ten percent of pregnancies, typically due to chromosomal abnormalities before the 12th week of pregnancy. Miscarriage (MĬS-kăr-ĭj) refers to loss of a fetus before the 20th week of pregnancy. It typically causes cramping and bleeding, similar to heavy menses. In some cases, a surgical procedure called dilation and evacuation (D&E) is required. Normal menstrual cycles generally return within a few months. [11]
Stillbirth (STĬL-bûrth), also known as intrauterine fetal demise (ĬN-tră-YŪ-tə-rīn FĒT-ăl dĕ-MĪZ), refers to the loss of a fetus after 20 weeks’ gestation. About 24,000 babies are stillborn every year in the United States. Although many times the cause of the stillbirth is unknown, there are several potential causes, such as infection, chromosomal abnormalities, placental problems. [12]
- This work is a derivative of Lifespan Development by Julie Lazzara and is licensed under CC BY 4.0 ↵
- This work is a derivative of StatPearls by Wormer, Jamil, & Bryant and is licensed under CC BY 4.0 ↵
- Centers for Disease Control and Prevention. (2023, April 26). Preventing pregnancy-related deaths. Department of Health & Human Services. www.cdc.gov/reproductivehealth/maternal-mortality/preventing-pregnancy-related-deaths.html ↵
- This work is a derivative of Lifespan Development by Julie Lazzara and is licensed under CC BY 4.0 ↵
- This work is a derivative of Lifespan Development by Julie Lazzara and is licensed under CC BY 4.0 ↵
- “ Photo_of_baby_with_FAS.jpg ” by Teresa Kellerman is licensed under CC BY-SA 3.0 ↵
- This work is a derivative of Lifespan Development by Julie Lazzara and is licensed under CC BY 4.0 ↵
- This work is a derivative of StatPearls by Suman & Luther and is licensed under CC BY 4.0 ↵
- This work is a derivative of StatPearls by Suman & Luther and is licensed under CC BY 4.0 ↵
- The American College of Obstetricians and Gynecologists. (2021, November). Cervical cerclage . https://www.acog.org/womens-health/faqs/cervical-cerclage ↵
- This work is a derivative of Lifespan Development by Julie Lazzara and is licensed under CC BY 4.0 ↵
- This work is a derivative of Lifespan Development by Julie Lazzara and is licensed under CC BY 4.0 ↵


22.5: Beneficial Prokaryotes
Skills to Develop
- Explain the need for nitrogen fixation and how it is accomplished
- Identify foods in which prokaryotes are used in the processing
- Describe the use of prokaryotes in bioremediation
- Describe the beneficial effects of bacteria that colonize our skin and digestive tracts
Not all prokaryotes are pathogenic. On the contrary, pathogens represent only a very small percentage of the diversity of the microbial world. In fact, our life would not be possible without prokaryotes. Just think about the role of prokaryotes in biogeochemical cycles.
Cooperation between Bacteria and Eukaryotes: Nitrogen Fixation
Nitrogen is a very important element to living things, because it is part of nucleotides and amino acids that are the building blocks of nucleic acids and proteins, respectively. Nitrogen is usually the most limiting element in terrestrial ecosystems, with atmospheric nitrogen, N 2 , providing the largest pool of available nitrogen. However, eukaryotes cannot use atmospheric, gaseous nitrogen to synthesize macromolecules. Fortunately, nitrogen can be “fixed,” meaning it is converted into ammonia (NH 3 ) either biologically or abiotically. Abiotic nitrogen fixation occurs as a result of lightning or by industrial processes.
Biological nitrogen fixation (BNF) is exclusively carried out by prokaryotes: soil bacteria, cyanobacteria, and Frankia spp. (filamentous bacteria interacting with actinorhizal plants such as alder, bayberry, and sweet fern). After photosynthesis, BNF is the second most important biological process on Earth. The equation representing the process is as follows
where Pi stands for inorganic phosphate. The total fixed nitrogen through BNF is about 100 to 180 million metric tons per year. Biological processes contribute 65 percent of the nitrogen used in agriculture.
Cyanobacteria are the most important nitrogen fixers in aquatic environments. In soil, members of the genus Clostridium are examples of free-living, nitrogen-fixing bacteria. Other bacteria live symbiotically with legume plants, providing the most important source of BNF. Symbionts may fix more nitrogen in soils than free-living organisms by a factor of 10. Soil bacteria, collectively called rhizobia, are able to symbiotically interact with legumes to form nodules , specialized structures where nitrogen fixation occurs (Figure \(\PageIndex{1}\)). Nitrogenase, the enzyme that fixes nitrogen, is inactivated by oxygen, so the nodule provides an oxygen-free area for nitrogen fixation to take place. This process provides a natural and inexpensive plant fertilizer, as it reduces atmospheric nitrogen to ammonia, which is easily usable by plants. The use of legumes is an excellent alternative to chemical fertilization and is of special interest to sustainable agriculture, which seeks to minimize the use of chemicals and conserve natural resources. Through symbiotic nitrogen fixation, the plant benefits from using an endless source of nitrogen: the atmosphere. Bacteria benefit from using photosynthates (carbohydrates produced during photosynthesis) from the plant and having a protected niche. Additionally, the soil benefits from being naturally fertilized. Therefore, the use of rhizobia as biofertilizers is a sustainable practice.
Why are legumes so important? Some, like soybeans, are key sources of agricultural protein. Some of the most important grain legumes are soybean, peanuts, peas, chickpeas, and beans. Other legumes, such as alfalfa, are used to feed cattle.
Early Biotechnology: Cheese, Bread, Wine, Beer, and Yogurt
According to the United Nations Convention on Biological Diversity, biotechnology is “any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use." 1 The concept of “specific use” involves some sort of commercial application. Genetic engineering, artificial selection, antibiotic production, and cell culture are current topics of study in biotechnology. However, humans have used prokaryotes before the term biotechnology was even coined. In addition, some of the goods and services are as simple as cheese, bread, wine, beer, and yogurt, which employ both bacteria and other microbes, such as yeast, a fungus (Figure \(\PageIndex{2}\)).
Cheese production began around 4,000–7,000 years ago when humans began to breed animals and process their milk. Fermentation in this case preserves nutrients: Milk will spoil relatively quickly, but when processed as cheese, it is more stable. As for beer, the oldest records of brewing are about 6,000 years old and refer to the Sumerians. Evidence indicates that the Sumerians discovered fermentation by chance. Wine has been produced for about 4,500 years, and evidence suggests that cultured milk products, like yogurt, have existed for at least 4,000 years.
Using Prokaryotes to Clean up Our Planet: Bioremediation
Microbial bioremediation is the use of prokaryotes (or microbial metabolism) to remove pollutants. Bioremediation has been used to remove agricultural chemicals (pesticides, fertilizers) that leach from soil into groundwater and the subsurface. Certain toxic metals and oxides, such as selenium and arsenic compounds, can also be removed from water by bioremediation. The reduction of SeO 4 -2 to SeO 3 -2 and to Se 0 (metallic selenium) is a method used to remove selenium ions from water. Mercury is an example of a toxic metal that can be removed from an environment by bioremediation. As an active ingredient of some pesticides, mercury is used in industry and is also a by-product of certain processes, such as battery production. Methyl mercury is usually present in very low concentrations in natural environments, but it is highly toxic because it accumulates in living tissues. Several species of bacteria can carry out the biotransformation of toxic mercury into nontoxic forms. These bacteria, such as Pseudomonas aeruginosa , can convert Hg +2 into Hg 0 , which is nontoxic to humans.
One of the most useful and interesting examples of the use of prokaryotes for bioremediation purposes is the cleanup of oil spills. The importance of prokaryotes to petroleum bioremediation has been demonstrated in several oil spills in recent years, such as the Exxon Valdez spill in Alaska (1989) (Figure \(\PageIndex{3}\)), the Prestige oil spill in Spain (2002), the spill into the Mediterranean from a Lebanon power plant (2006), and more recently, the BP oil spill in the Gulf of Mexico (2010). To clean up these spills, bioremediation is promoted by the addition of inorganic nutrients that help bacteria to grow. Hydrocarbon-degrading bacteria feed on hydrocarbons in the oil droplet, breaking down the hydrocarbons. Some species, such as Alcanivorax borkumensis , produce surfactants that solubilize the oil, whereas other bacteria degrade the oil into carbon dioxide. In the case of oil spills in the ocean, ongoing, natural bioremediation tends to occur, inasmuch as there are oil-consuming bacteria in the ocean prior to the spill. In addition to naturally occurring oil-degrading bacteria, humans select and engineer bacteria that possess the same capability with increased efficacy and spectrum of hydrocarbon compounds that can be processed. Under ideal conditions, it has been reported that up to 80 percent of the nonvolatile components in oil can be degraded within one year of the spill. Other oil fractions containing aromatic and highly branched hydrocarbon chains are more difficult to remove and remain in the environment for longer periods of time.
Everyday Connection: Microbes on the Human Body
The commensal bacteria that inhabit our skin and gastrointestinal tract do a host of good things for us. They protect us from pathogens, help us digest our food, and produce some of our vitamins and other nutrients. These activities have been known for a long time. More recently, scientists have gathered evidence that these bacteria may also help regulate our moods, influence our activity levels, and even help control weight by affecting our food choices and absorption patterns. The Human Microbiome Project has begun the process of cataloging our normal bacteria (and archaea) so we can better understand these functions.
A particularly fascinating example of our normal flora relates to our digestive systems. People who take high doses of antibiotics tend to lose many of their normal gut bacteria, allowing a naturally antibiotic-resistant species called Clostridium difficile to overgrow and cause severe gastric problems, especially chronic diarrhea (Figure \(\PageIndex{4}\)). Obviously, trying to treat this problem with antibiotics only makes it worse. However, it has been successfully treated by giving the patients fecal transplants from healthy donors to reestablish the normal intestinal microbial community. Clinical trials are underway to ensure the safety and effectiveness of this technique.
Scientists are also discovering that the absence of certain key microbes from our intestinal tract may set us up for a variety of problems. This seems to be particularly true regarding the appropriate functioning of the immune system. There are intriguing findings that suggest that the absence of these microbes is an important contributor to the development of allergies and some autoimmune disorders. Research is currently underway to test whether adding certain microbes to our internal ecosystem may help in the treatment of these problems as well as in treating some forms of autism.
Summary
Pathogens are only a small percentage of all prokaryotes. In fact, our life would not be possible without prokaryotes. Nitrogen is usually the most limiting element in terrestrial ecosystems; atmospheric nitrogen, the largest pool of available nitrogen, is unavailable to eukaryotes. Nitrogen can be “fixed,” or converted into ammonia (NH 3 ) either biologically or abiotically. Biological nitrogen fixation (BNF) is exclusively carried out by prokaryotes. After photosynthesis, BNF is the second most important biological process on Earth. The most important source of BNF is the symbiotic interaction between soil bacteria and legume plants.
Microbial bioremediation is the use of microbial metabolism to remove pollutants. Bioremediation has been used to remove agricultural chemicals that leach from soil into groundwater and the subsurface. Toxic metals and oxides, such as selenium and arsenic compounds, can also be removed by bioremediation. Probably one of the most useful and interesting examples of the use of prokaryotes for bioremediation purposes is the cleanup of oil spills.
Human life is only possible due to the action of microbes, both those in the environment and those species that call us home. Internally, they help us digest our food, produce crucial nutrients for us, protect us from pathogenic microbes, and help train our immune systems to function correctly.
Footnotes
- 1 http://www.cbd.int/convention/articles/?a=cbd-02 , United Nations Convention on Biological Diversity: Article 2: Use of Terms.
Glossary
- biological nitrogen fixation
- conversion of atmospheric nitrogen into ammonia exclusively carried out by prokaryotes
- bioremediation
- use of microbial metabolism to remove pollutants
- biotechnology
- any technological application that uses living organisms, biological systems, or their derivatives to produce or modify other products
- nodule
- novel structure on the roots of certain plants (legumes) that results from the symbiotic interaction between the plant and soil bacteria, is the site of nitrogen fixation


5.1.4: Opportunities and Challenges to Team Building
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Learning Objectives
- What are the benefits of conflict for a team?
There are many sources of conflict for a team, whether it is due to a communication breakdown, competing views or goals, power struggles, or conflicts between different personalities. The perception is that conflict is generally bad for a team and that it will inevitably bring the team down and cause them to spiral out of control and off track. Conflict does have some potential costs. If handled poorly, it can create distrust within a group, it can be disruptive to group progress and moral, and it could be detrimental to building lasting relationships. It is generally seen as a negative, even though constructive conflicts and constructive responses to conflicts can be an important developmental milestone for a team. Some potential benefits of conflict are that it encourages a greater diversity of ideas and perspectives and helps people to better understand opposing points of view. It can also enhance a team’s problem-solving capability and can highlight critical points of discussion and contention that need to be given more thought.
Another key benefit or outcome of conflict is that a team that trusts each other—its members and members’ intentions—will arise from conflict being a stronger and higher-performing team. Patrick Lencioni, in his bestselling book The Five Dysfunctions of a Team (2002, p. 188), writes: 7
“The first dysfunction is an absence of trust among team members. Essentially, this stems from their unwillingness to be vulnerable within the group. Team members who are not genuinely open with one another about their mistakes and weaknesses make it impossible to build a foundation for trust. This failure to build trust is damaging because it sets the tone for the second dysfunction: fear of conflict. Teams that lack trust are incapable of engaging in unfiltered and passionate debate of ideas. Instead, they resort to veiled discussions and guarded comments.”
Lencioni also asserts that if a team doesn’t work through its conflict and air its opinions through debate, team members will never really be able to buy in and commit to decisions. (This lack of commitment is Lencioni’s third dysfunction.) Teams often have a fear of conflict so as not to hurt any team members’ feelings. The downside of this avoidance is that conflicts still exist under the surface and may resurface in more insidious and back-channel ways that can derail a team. How can a team overcome its fear of conflict and move the team forward? Lencioni names a few strategies that teams can use to make conflict more common and productive. Mining is a technique that can be used in teams that tend to avoid conflict. This technique requires that one team member “assume the role of a ‘miner of conflict’—someone who extracts buried disagreements within the team and sheds the light of day on them. They must have the courage and confidence to call out sensitive issues and force team members to work through them.” Real-time permission is another technique to “recognize when the people engaged in conflict are becoming uncomfortable with the level of discord, and then interrupt to remind them that what they are doing is necessary.” This technique can help the group to focus on the points of conflict by coaching the team not to sweep things under the rug.
The team leader plays a very important role in the team’s ability to address and navigate successfully through conflicts. Sometime a leader will have the attitude that conflict is a derailer and will try to stymie it at any cost. This ultimately leads to a team culture in which conflict is avoided and the underlying feelings are allowed to accumulate below the surface of the discussion. The leader should, by contrast, model the appropriate behavior by constructively addressing conflict and bringing issues to the surface to be addressed and resolved by the team. This is key to building a successful and effective team.
There are a variety of individual responses to conflict that you may see as a team member. Some people take the constructive and thoughtful path when conflicts arise, while others may jump immediately to destructive behaviors. In Managing Conflict Dynamics: A Practical Approach , Capobianco, Davis, and Kraus (2005) recognized that there are both constructive and destructive responses to conflict, as well as active and passive responses that we need to recognize. In the event of team conflict, the goal is to have a constructive response in order to encourage dialogue, learning, and resolution. 8 Responses such as perspective taking, creating solutions, expressing emotions, and reaching out are considered active and constructive responses to conflict. Reflective thinking, delay responding, and adapting are considered passive and constructive responses to conflict. See Figure 15.4.1 for a visual of the constructive responses, as well as the destructive responses, to conflict.
In summary, conflict is never easy for an individual or a team to navigate through, but it can and should be done. Illuminating the team about areas of conflict and differing perspectives can have a very positive impact on the growth and future performance of the team, and it should be managed constructively.
concept check
- What are some techniques to make conflict more productive?
- What are some destructive responses to conflict?