Therapist competence in global mental health: Development of the ENhancing Assessment of Common Therapeutic factors (ENACT) rating scale
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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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".