Treating depression with cognitive behavioural therapy: exploring therapist technique
Bibliographic record
Abstract
A seriously debilitating condition, major depressive disorder (MDD) is the most common psychiatric illness (Kleine-Budde et al., 2013) said to become the foremost contributor to disease burden in high income countries like Canada by 2030 (Mathers & Loncar, 2006). Cognitive behavioural therapy (CBT) is a widely used treatment with ample support for its efficacy in treating depression. It is based on the notion that depression is maintained chiefly by dysfunctional beliefs that influence motivation, behaviour, and affect (Beck, Rush, Shaw, & Emery, 1979). As such, it is known for its abundance of goal-oriented, systematic interventions aimed at modifying everyday thoughts, behaviours, and emotions to alleviate symptoms of depression (Keshi, Basavarajappa, & Nik, 2013). Decades of outcome studies demonstrate CBT's effectiveness in treating depression (Butler, Chapman, Forman, & Beck, 2006). However, researchers remain confused about the specific elements responsible for its success (Webb, Auerbach, & DeRubeis, 2012). While there have been numerous efforts toward better understanding the mechanisms of change in CBT, much of this body of research has been criticised for its methodological or conceptual limitations (Drapeau, 2014). This dissertation presents two distinct studies that serve to address some of these limitations to better our understanding of the specific therapist behaviours that contribute to patient improvement. The first examines specific interventions that occur in CBT with depressed patients and assesses their individual relationships with symptoms of depression, and late therapy cognitive errors and overall coping functioning. Addressing the persistent common vs. specific factor debate in the psychotherapy community, it identifies both interventions specific to CBT, as well as some common to all therapies related to the alliance. The second study is an extension of the first, exploring the focus of therapist interventions on two pivotal concepts in CBT: cognitive errors and coping strategies. Inspired by methods used in psychodynamic research, it presents a method to gauge therapist accuracy in CBT based on therapist-patient interaction on these concepts. The relationships between therapist accuracy conceptualized in this way and the same three dependent variables are investigated. Practical clinical and research implications of both studies are discussed throughout the dissertation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".