Therapist competence in global mental health: Development of the ENhancing Assessment of Common Therapeutic factors (ENACT) rating scale
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
Lack of reliable and valid measures of therapist competence is a barrier to dissemination and implementation of psychological treatments in global mental health. We developed the ENhancing Assessment of Common Therapeutic factors (ENACT) rating scale for training and supervision across settings varied by culture and access to mental health resources. We employed a four-step process in Nepal: (1) Item generation: We extracted 1081 items (grouped into 104 domains) from 56 existing tools; role-plays with Nepali therapists generated 11 additional domains. (2) Item relevance: From the 115 domains, Nepali therapists selected 49 domains of therapeutic importance and high comprehensibility. (3) Item utility: We piloted the ENACT scale through rating role-play videotapes, patient session transcripts, and live observations of primary care workers in trainings for psychological treatments and the Mental Health Gap Action Programme (mhGAP). (4) Inter-rater reliability was acceptable for experts (intraclass correlation coefficient, ICC(2,7) = 0.88 (95% confidence interval (CI) 0.81-0.93), N = 7) and non-specialists (ICC(1,3) = 0.67 (95% CI 0.60-0.73), N = 34). In sum, the ENACT scale is an 18-item assessment for common factors in psychological treatments, including task-sharing initiatives with non-specialists across cultural settings. Further research is needed to evaluate applications for therapy quality and association with patient outcomes.
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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 | 0.002 | 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