Scaling up quality-assured psychotherapy: The role of therapist competence on perinatal depression and anxiety outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it