Technology-assisted task-sharing to bridge the treatment gap for childhood developmental disorders in rural Pakistan: an implementation science case study
Bibliographic record
Abstract
BACKGROUND: As in many low-income countries, the treatment gap for developmental disorders in Pakistan is nearly 100%. The World Health Organization (WHO) has developed the mental Health Gap Intervention guide (mhGAP-IG) to train non-specialists in the delivery of evidence-based mental health interventions in low-resource settings. However, a key challenge to scale-up of non-specialist-delivered interventions is designing training programs that promote fidelity at scale in low-resource settings. In this case study, we report the experience of using a tablet device-based application to train non-specialist, female family volunteers in leading a group parent skills training program, culturally adapted from the mhGAP-IG, with fidelity at scale in rural community settings of Pakistan. METHODS: The implementation evaluation was conducted as a part of the mhGAP-IG implementation in the pilot sub-district of Gujar Khan. Family volunteers used a technology-assisted approach to deliver the parent skills training in 15 rural Union Councils (UCs). We used the Proctor and RE-AIM frameworks in a mixed-methods design to evaluate the volunteers' competency and fidelity to the intervention. The outcome was measured with the ENhancing Assessment of Common Therapeutic factors (ENACT), during training and program implementation. Data on other implementation outcomes including intervention dosage, acceptability, feasibility, appropriateness, and reach was collected from program trainers, family volunteers, and caregivers of children 6 months post-program implementation. Qualitative and quantitative data were analyzed using the framework and descriptive analysis, respectively. RESULTS: We trained 36 volunteers in delivering the program using technology. All volunteers were female with a mean age of 39 (± 4.38) years. The volunteers delivered the program to 270 caregivers in group sessions with good fidelity (scored 2.5 out of 4 on each domain of the fidelity measure). More than 85% of the caregivers attended 6 or more of 9 sessions. Quantitative analysis showed high levels of acceptability, feasibility, appropriateness, and reach of the program. Qualitative results indicated that the use of tablet device-based applications, and the cultural appropriateness of the adapted intervention content, contributed to the successful implementation of the program. However, barriers faced by family volunteers like community norms and family commitments potentially limited their mobility to deliver the program and impacted the program' reach. CONCLUSIONS: Technology can be used to train non-specialist family volunteers in delivering evidence-based intervention at scale with fidelity in low-resource settings of Pakistan. However, cultural and gender norms should be considered while involving females as volunteer lay health workers for the implementation of mental health programs 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".