A Digital Tool (Technology-Assisted Problem Management Plus) for Lay Health Workers to Address Common Mental Health Disorders: Co-production and Usability Study in Pakistan
Bibliographic record
Abstract
BACKGROUND: Mental health remains among the top 10 leading causes of disease burden globally, and there is a significant treatment gap due to limited resources, stigma, limited accessibility, and low perceived need for treatment. Problem Management Plus, a World Health Organization-endorsed brief psychological intervention for mental health disorders, has been shown to be effective and cost-effective in various countries globally but faces implementation challenges, such as quality control in training, supervision, and delivery. While digital technologies to foster mental health care have the potential to close treatment gaps and address the issues of quality control, their development requires context-specific, interdisciplinary, and participatory approaches to enhance impact and acceptance. OBJECTIVE: We aimed to co-produce Technology-Assisted Problem Management Plus (TA-PM+) for "lady health workers" (LHWs; this is the terminology used by the Lady Health Worker Programme for lay health workers) to efficiently deliver sessions to women with symptoms of common mental health disorders within the community settings of Pakistan and conducted usability testing in community settings. METHODS: A 3-stage framework was used for co-producing and prototyping the intervention. Stage 1 (evidence review and stakeholder consultation) included 3 focus group discussions with 32 LHWs and 7 in-depth interviews with key stakeholders working in the health system or at the health policy level. Thematic analyses using the Capability, Opportunity, and Motivation for Behavioral Change (COM-B) model were conducted. Stage 2 included over eight online workshops, and a multidisciplinary intervention development group co-produced TA-PM+. Stage 3 (prototyping) involved 2 usability testing rounds. In round 1 conducted in laboratory settings, 6 LHWs participated in role plays and completed the 15-item mHealth Usability App Questionnaire (MUAQ) (score range 0-7). In round 2 conducted in community settings, trained LHWs delivered the intervention to 6 participants screened for depression and anxiety. Data were collected using the MUAQ completed by LHWs and the Patient Satisfaction Questionnaire (PSQ) (score range 0-46) completed by participants. RESULTS: Qualitative analysis indicated that a lack of digital skills among LHWs, high workload, resource scarcity for digitization (specifically internet bandwidth in the community), and need for comprehensive training were barriers for TA-PM+ implementation in the community through LHWs. Training, professional support, user guidance, an easy and automated interface, offline functionalities, incentives, and strong credibility among communities were perceived to enhance the capability, opportunity, and motivation of LHWs to implement TA-PM+. TA-PM+ was co-produced with features like an automated interface, a personal dashboard, guidance videos, and a connected supervisory panel. The mean MUAQ score was 5.62 in round 1 of usability testing and improved to 5.96 after incorporating LHW feedback in round 2. The mean PSQ score for TA-PM+ was 40 in round 2. CONCLUSIONS: Co-production of TA-PM+ for LHWs balanced context and evidence. The 3-stage iterative development approach resulted in high usability and acceptability of TA-PM+ for LHWs and participants.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".