Improving the scalability of psychological treatments in developing countries: An evaluation of peer-led therapy quality assessment in Goa, India
Bibliographic record
Abstract
Psychological treatments delivered by lay therapists, with little or no previous mental health training, have been shown to be effective in treating a range of mental health problems. In low resource settings, the dearth of available experts to assess therapy quality potentially leads to a bottleneck in scaling up lay therapist delivered psychological treatments. Peer-led supervision and the assessment of therapy quality may be one solution to address this barrier. The purpose of this study was two-fold: 1) to assess lay therapist quality ratings compared to expert supervisors in a multisite study where lay therapists delivered two locally developed, psychological treatments for harmful and dependent drinking and severe depression; 2) assess the acceptability and feasibility of peer-led supervision compared to expert-led supervision. We developed two scales, one for each treatment, to compare lay therapist and expert ratings on audio-taped treatment sessions (n = 189). Our findings confirmed our primary hypothesis of increased levels of agreement between peer and expert ratings over three consecutive time periods as demonstrated by a decrease in the differences in mean therapy quality rating scores. This study highlights that lay therapists can be trained to effectively assess each other's therapy sessions as well as experts, and that peer-led supervision is acceptable for lay therapists, thus, enhancing the scalability of psychological treatments in low-resource settings.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".