Data analysis
REGRESSION
Survey responses were summarised using descriptive statistics (mean, median and standard deviations [SD], counts and percentages). We used a multinomial logistic (MNL) regression model to rank the 60 recommendations according to those participants who were most and least willing to follow them. Preference scores were based on the mean regression coefficients and 95% confidence intervals (CIs). For ease of interpretation, we calculated outcome level preference scores by normalizing the mean coefficients to a 0–10 scale, where 0 was the least and 10 the most preferred recommendation (‘normalised preference scores’) across all recommendations. Given the large number of recommendations, the marginal effects were calculated to assess the relative importance of different characteristics of the recommendations as well as the influence of sex, clinical area of interest, years of experience, familiarity with the recommendations, work setting, and involvement in research teaching or other professional activities on the preference scores. Marginal effects were calculated from the linear regression of preference scores with recommendation characteristics and subgroup analyses based on participant characteristics. A marginal effect describes the influence of the presence or absence of a characteristic on the preference score when all other variables are held at the average value. A positive value indicates that the characteristic increases preference scores while the opposite is the case for negative values. As all variables are on the same scale, the marginal effects can be directly compared thereby providing a basis for estimating the relative impact on preference across the 60 recommendations. We also described the ranking of the six original recommendations compared to the most preferred recommendation across the six topics of the original recommendations. Multinomial logit regression estimations were undertaken using NLOGIT V6 and linear regression and marginal effects using Stata Release 17.