For this observational study we had the participation of a 31-year-old client diagnosed with borderline personality disorder (BPD) for the last 8 years; and a 35-year-old clinical psychologist (a master's degree in General Health Psychology and a master's degree in Behavior Therapy). Both participants came from a public-funded Vocational Rehabilitation and Employment center (VR&E) in the Community of Madrid. The client has been referred to this center due to mood problems (i.e., emotional lability) and substance use problems that directly interfere with the client's chances of accessing employment/training opportunities; and once obtained, problems in keeping his job or completing the required training. Also, this study involved the participation of two trained observers. Both observers are predoctoral students that have been trained in the same research group (i.e., the ACOVEO research group). Observer 1 and Observer 2 have, respectively, 4 and 2 years of experience working with the observational coding system used in this research and they helped in the development of it. Before recording the clinical session, the client and clinician have been informed about the use of the data and the purpose of the research. All participants have signed the study informed consent.
We used the Functional Coding System for Verbal Interaction in Clinical Contexts to code the verbal interaction between the client and the psychologists. This coding system, which focuses on the putative functions of the verbal behavior, has five coding categories for the clinician verbalizations: Clinical Discriminative Stimulus (CD) and Instructional Discriminative Stimulus (ID), Conditioned Motivating Operation (CMO), and Positive Reinforcer (R+) and Aversive Stimulus (AS). The observational system assumes that the verbal behavior of the client has a response function it is not established any specific categories in the observational system. To code the client's verbal behavior in this case we used eight categories based on the response topography Giving Information (GI), Asking for Information (AI), Following Instructions (FI), Not Following Instructions (NFI), Well-Being (WB), Discomfort (D), Target Behavior (TB) and Problem Behavior (PB). See Table 1 for a brief description of the coding categories, also more details of the coding system are available in the additional materials.
Recordings of 16 clinical sessions were obtained through the camera installed on the VR&E psychologist's computer. The recordings were sent to the ACOVEO research group and were treated following the protocol used in the research group in which the recordings are anonymized following the ethical and legal guidelines of the Organic Law 3/2018 on Personal Data Protection and guarantee of digital rights. These recordings were stored on external hard drives kept under lock and key in the group's laboratory at the Autonomous University of Madrid.
The recording of the clinical sessions, the observation project, and the analysis of inter-observer reliability were carried out with The Observer XT 12 observation software. The data analysis was done in R (RStudio Team, 2020) and Microsoft Excel. GSEQ was the software used to conduct the sequential analysis of the data. We selected this software because it is generally employed in observational research (e.g.,); it was specifically used in previous research in the study of verbal interaction in clinical cases; and it was specially developed to calculate sequential patterns in observational data. Finally, we used ThemeEdu software for automatic pattern detection. ThemeEdu was selected because it has been successfully applied in different research areas (i.e., neuronal interactions, behavioral interactions, etc.), but it was not employed in the study of verbal interactions in clinical settings. Both programs allowed us to conduct the data analysis described in the section below (see "Data Analysis").
In this observational study, we used an intra-subject design with three different phases: Evaluation (EVA.), Treatment Phase 1 (T1), and Treatment Phase 2 (T2). These phases were not experimentally manipulated and have been divided considering the protocols and procedures of the VR&E center (i.e., EVA, first three sessions; TP1, 4-9 sessions; and TP2, 10-16 sessions). The division between the two treatment phases is arbitrary and responds to a need to divide the treatment into, at least, two phases to evaluate differences between various time points.
After the study, both observers received specific training in the observational instrument. The training process was completed when a stable reliability index (k > 0.70) was achieved while coding similar clinical sessions. These sessions were from the research team's clinical sessions archive.
After training was completed, observer 1 individually recorded all treatment sessions. Observer 2, also individually, recorded 4 random sessions out of the 16 treatment sessions, representing 25% of the sample, which is above the usual 10% for studies of this type. The inter-observer reliability calculation was carried out after the end of the recording of both observers. One month after the end of the recording phase, observer 1 recorded again two randomly selected treatment sessions to allow the calculation of intra-observer reliability.
The kappa coefficient (k) was used to calculate the inter-observer and intra-observer reliability of the records. Once the records were obtained, the rate per minute of each variable recorded in each session was calculated. Descriptive data (e.g., count, rate per minute, etc.) were obtained to allow a visual inspection of the variables and the patterns through time. To analyze the interaction between variables, we used two types of statistical analysis, which are part of the family of statistical tools for the sequential analysis of temporally distributed data. First, we calculated the Yule's Q, a contingency index of 2x2 tables, using the GSEQ software. This index allows us to calculate the Lag +1 correlation between a given behavior and the one that follows it. The Lag-1 correlation tells us which behavior precedes the behavior we are analyzing. This index allows descriptive and analytical analysis of the association and its scores can also be interpreted in the same way as Cohen's r. To study the association between specific pairs of behaviors we calculated the adjusted residuals (z), which are a normalized index of the extent to which the values of the frequencies observed in each cell of the matrix deviate from their expected values: a value greater than 1.96 indicates that this behavior occurs significantly more than expected and, conversely, a value less than -1.96 implies that it occurs significantly less than expected by chance (p < 0.05; see for an advance mathematical description).
Moreover, we used a pattern recognition model T-Pattern Model, using ThemeEdu software. This model allows us to recognize patterns (T-Patterns) from observational data (T-Data) using the T-Pattern detection algorithm. Pattern detection works in a bottom-up fashion, from the data to the pattern detection. The T-Pattern is a hierarchical, multi-ordinal, and self-similar pattern type that comprises m ordered components (i.e., behavioral events), X1.m, recurring in a single discrete dimension, where each component is a T-data category (or pattern primitive, called event-type) or a T-pattern.
In this case, patterns of interaction between client and therapist have been detected in all three phases of observation. We used the pattern recognition default settings with some specifications. We have required that the patterns must be repeated at least three times in each session and all sessions of each phase, with this we want to make sure that the pattern is a characteristic of this phase not a characteristic of one of the sessions. Also, we have excluded the patterns made by the repetition of the same variable and the patterns made by an interaction of variables of the same subject, because we want to study the interaction between variables. Finally, we have required a maximum of 4 levels for the analysis of the interaction, because more than four levels could be interpreted as chains made of contingencies of two or three members.
To assess the effect of the treatment on the client's behaviors we calculated the effect size, in this study we used the Non-overlap of All Pairs (NAP) which is an index focused on identifying the differences between two phases of a design (A and B).