Scaling up quality-assured psychotherapy: The role of therapist competence on perinatal depression and anxiety outcomes
Bibliographic record
Abstract
OBJECTIVES: To examine: (1) the psychometric properties of two therapist competence measures-multiple choice questionnaire (MCQ) and standardized role-plays; (2) whether therapist competence differed between non-specialist (NSPs) and specialist (SPs) providers; and (3) the relations between therapist competence and patient outcomes among perinatal patients receiving brief psychotherapy. METHODS: This study is embedded within the SUMMIT Trial-a large, ongoing psychotherapy trial for perinatal women with depressive and anxiety symptoms. We assessed the: (1) psychometric properties of therapist competence measures using Cronbach's alpha and inter-class correlation; (2) differences in therapist competence scores between n = 23 NSPs and n = 22 SPs using a two-sample t-test; and (3) relations between therapist competence measures and perinatal patient outcomes through a linear regression model. RESULTS: Internal consistency for role-play was acceptable (α = 0.71), whereas MCQ was excellent (α = 0.97). Role-play showed good inter-rater reliability (ICC = 0.80) and scores were higher for SPs compared with NSPs (t(2,38) = -2.86, p = 0.0069) and associated with outcomes of anxiety (B = 1.52, SE = 0.60, p = 0.01) and depressive (B = 0.96, SE = 0.55, p = 0.08) symptom scores. CONCLUSIONS: Our study highlights the importance of demonstrating psychological treatment skills through standardized role-plays over knowledge-based competence to predict perinatal patient outcomes. Using well-defined evidence-based tools is critical for deploying NSPs to provide high-quality psychotherapy and increase accessibility to psychological treatments for perinatal populations worldwide.
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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 | 0.021 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".