Using technology to scale-up training and supervision of community health workers in the psychosocial management of perinatal depression: a non-inferiority, randomized controlled trial
Bibliographic record
Abstract
Background. The Thinking Healthy Programme (THP) is an evidence-based psychological intervention endorsed by the World Health Organization, tailored for non-specialist health workers in low- and middle-income countries. However, training and supervision of large numbers of health workers is a major challenge for the scale-up of THP. We developed a ‘Technology-Assisted Cascaded Training and Supervision system’ (TACTS) for THP consisting of a training application and cascaded supervision delivered from a distance. Methods. A single-blind, non-inferiority, randomized controlled trial was conducted in District Swat, a post-conflict area of North Pakistan. Eighty community health workers (called Lady Health Workers or LHWs) were randomly assigned to either TACTS or conventional face-to-face training and supervision by a specialist. Competence of LHWs in delivering THP post-training was assessed by independent observers rating a therapeutic session using a standardized measure, the ‘Enhancing Assessment of Common Therapeutic factors’ (ENACT), immediately post-training and after 3 months. ENACT uses a Likert scale to score an observed interaction on 18 dimensions, with a total score of 54, and a higher score indicating greater competence. Results. Results indicated no significant differences between health workers trained using TACTS and supervised from distance v. those trained and supervised by a specialist face-to-face ( mean ENACT score M = 24.97, s.d. = 5.95 v . M = 27.27, s.d. = 5.60, p = 0.079, 95% CI 4.87–0.27) and at 3 months follow-up assessment ( M = 44.48, s.d. = 3.97 v . M = 43.63, s.d. = 6.34, p = 0.53, CI −1.88 to 3.59). Conclusions. TACTS can provide a promising tool for training and supervision of front-line workers in areas where there is a shortage of specialist trainers and supervisors.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".