Application of the ADAPT Framework to Contextualize a Participatory Learning and Action Community Intervention for the Prevention and Control of Type 2 Diabetes Mellitus in Urban and Rural Settings in Afghanistan and Pakistan: Protocol for Intervention Adaptation
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (T2DM) is a critical global health issue, especially in low- and middle-income countries like Afghanistan and Pakistan, where many cases remain undiagnosed and specialized care is costly. Community Health Participatory Research, which actively involves community members in addressing health issues, is increasingly recognized as an effective approach to deliver sustainable and culturally relevant health solutions. Participatory Learning and Action (PLA), a method of Community Health Participatory Research, was implemented in the D-Magic (Diabetes Mellitus: Action Through Community Groups or mHealth Information for Better Control) trial in Bangladesh that empowered communities to identify challenges and develop locally relevant strategies to prevent and control T2DM. Objective: This study aims to adapt the PLA-based D-Magic intervention for the prevention and control of T2DM in rural and urban communities of Afghanistan and Pakistan. Methods: This trial, titled "Engagement of Community Through Participatory Learning and Action for Control and Prevention of Type 2 Diabetes and Its Risk Factors" (EMPOWER-D), will systematically adapt the D-Magic intervention using the ADAPT guidance framework. The study will be conducted across 3 sites: rural Afghanistan (Kabul), rural Pakistan (Peshawar and Swabi), and urban Pakistan (Karachi). Stakeholders and community members will be involved to ensure cultural relevance and authenticity. The process includes identifying barriers and facilitators to the implementation of community-based interventions through a qualitative study and a scoping review, co-designing intervention materials, and piloting for feasibility and effectiveness. Results: EMPOWER-D was funded and launched in November 2022 by the United Kingdom's National Institute for Health and Care Research (NIHR203248). Following a year-long development phase, the adaptation protocol was finalized, and a mock run of the interviews was conducted. Recruitment and qualitative data collection, including in-depth interviews and focus group discussions, began in July 2024 and have been completed in Peshawar, Swabi, and Karachi. As of January 2025, data collection has commenced in Afghanistan. Results are anticipated to be published in mid-2026 following the completion of data analysis. Conclusions: The EMPOWER-D adaptation will align the PLA-based intervention with the local sociocultural context, ensuring health equity and participation. Building on the D-Magic trial's success, this adaptation will ensure the intervention's effectiveness and cost-effectiveness in preventing and managing T2DM, not only in Afghanistan and Pakistan but also in other low- and middle-income countries.
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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 | 0.080 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".