Designing an App to Support Measurement-Based Peer Supervision of Frontline Health Workers Delivering Brief Psychosocial Interventions in Texas: Multimethod Study
Bibliographic record
Abstract
BACKGROUND: The unmet need for mental health care affects millions of Americans. A growing body of evidence in implementation science supports the effectiveness of task sharing in the delivery of brief psychosocial interventions. The digitization of training and processes supporting supervision can rapidly scale up task-shared interventions and enable frontline health workers (FLWs) to learn, master, and deliver interventions with quality and support. OBJECTIVE: We aimed to assess the perceived feasibility and acceptability of a novel mobile and web app designed and adapted to support the supervision, training, and quality assurance of FLWs delivering brief psychosocial interventions. METHODS: We followed human-centered design principles to adapt a prototype app for FLWs delivering brief psychosocial interventions for depression, drawing from an app previously designed for use in rural India. Using a multimethod approach, we conducted focus group sessions comprising usability testing and group interviews with FLWs recruited from a large health system in Texas to assess the feasibility and acceptability of the app. The positive System Usability Scale was used to determine the app's overall usability. We also calculated the participants' likelihood of recommending the app to others using ratings of 0 to 10 from least to most likely (net promoter score). Focus group transcripts were coded and analyzed thematically, and recommendations were summarized across 4 key domains. RESULTS: A total of 18 FLWs varying in role and experience with client care participated in the study. Participants found the app to be usable, with an average System Usability Scale score of 72.5 (SD 18.1), consistent with the industry benchmark of 68. Participants' likelihood of recommending the app ranged from 5 to 10, yielding a net promoter score of 0, indicating medium acceptability. Overall impressions of the app from participants were positive. Most participants (15/18, 83%) found the app easy to access and navigate. The app was considered important to support FLWs in delivering high-quality mental health care services. Participants felt that the app could provide more structure to FLW training and supervision processes through the systematic collection and facilitation of performance-related feedback. Key concerns included privacy-related and time constraints regarding implementing a separate peer supervision mechanism that may add to FLWs' workloads. CONCLUSIONS: We designed, built, and tested a usable, functional mobile and web app prototype that supports FLW-delivered psychosocial interventions in the United States through a structured supervision mechanism and systematic collection and review of performance measures. The app has the potential to scale the work of FLWs tasked with delivering these interventions to the hardest-to-reach communities they serve. The results of this project will inform future work to evaluate the app's use and efficacy in real-world settings to support task-shared mental health programs across the United States.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".