The influence and interaction of client and therapist interpersonal variables in cognitive therapy for major depressive disorder
Bibliographic record
Abstract
Major depressive disorder (MDD) is a severe illness associated with impairment in key areas of functioning (American Psychiatric Association, 2013) and burdensome societal costs (Lim, Jacobs, Ohinmaa, Schopflocher, & Dewa, 2008). It is a substantial contributor to the global burden of disease (World Health Organization, 2008), and recent estimates indicate that nearly 20% of individuals living in Canada and the United States will experience at least one depressive episode in their lifetime (Kessler & Bromet, 2013; Patten, 2009). In addition, the majority of individuals who become depressed will experience a subsequent episode of depression (American Psychiatric Association, 2000; Solomon et al., 2000). Cognitive therapy (CT) has received substantial empirical support for the treatment of depression (Butler, Chapman, Forman, & Beck, 2006). However, CT does not benefit all patients equally, and treatment outcome appears to be related, at least in part, to interpersonal variables (Beutler, Castonguay, & Follette, 2006; Hardy et al., 2001; Keijsers, Schaap, & Hoogduin, 2000). For example, depressed patients’ interpersonal style can influence the development of the therapeutic alliance, a major predictor of treatment outcome, as well as the overall effectiveness of cognitive treatment (Hardy et al., 2001; McEvoy, Burgess, & Nathan, 2013; Renner et al., 2012). Likewise, therapists’ interpersonal style along with their use of treatment techniques appears to shape the process and outcome of CT for depression (Beutler et al., 2006; Keijsers et al., 2000). Researchers are increasingly attentive to the relationship between interpersonal variables and treatment outcome in CT for depression (Grosse Holtforth et al., 2013; Saatsi, Hardy, & Cahill, 2007). This dissertation also examines the relationship between client and therapist interpersonal variables and treatment outcome in CT for MDD. Three distinct studies 1) describe client and therapist interpersonal behaviour in this modality and investigate their effect on outcome; 2) identify changes in clients’ self-talk occurring over the course of treatment and determine their influence on outcome; and 3) examine the dyadic interaction of clients and therapists using sequential analyses. The clinical and research implications of these studies are discussed throughout the dissertation.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".