Author comment: Scaling up the task-sharing of psychological therapies: A formative study of the PEERS smartphone application for supervision and quality assurance in rural India — R1/PR4
Bibliographic record
Abstract
Measurement-based peer supervision is one strategy to assure the quality of psychological treatments delivered by non-mental health specialist providers. In this formative study, we aimed to 1) describe the development and 2) examine the acceptability and feasibility of PEERS (Promoting Effective mental healthcare through peER Supervision)—a novel smartphone app that aims to facilitate registering and scheduling patients, collecting patient outcomes, rating therapy quality and assessing supervision quality—among frontline treatment providers delivering behavioral activation treatment for depression. The PEERS prototype was developed and tested in 2021, and version 1 was launched in 2022. To date, 215 treatment providers (98% female; ages 30–35) in Madhya Pradesh and Goa, India, have been trained to use PEERS and 65.58% have completed the supplemental, virtual PEERS course. Focus group discussions with 98 providers were examined according to four themes—training and education, app effectiveness, user experience and adherence and data privacy and safety. This yielded commonly endorsed facilitators (e.g., collaborative learning through group supervision, the convenience of consolidated patient data), barriers (e.g., difficulties with new technologies) and suggested changes (e.g., esthetic improvements, suicide risk assessment prompt). The PEERS app has the potential to scale measurement-based peer supervision to facilitate quality assurance of psychological treatments across contexts.
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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 | 0.018 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".