Mapping Respiratory Health Digital Interventions in South and Southeast Asia: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The last 2 decades have been a time of exponential growth and maturation for digital health, while the global burden of respiratory disease continues to grow worldwide. Leveraging digital health interventions (DHIs) to manage and mitigate respiratory disease and its adverse health effects presents itself as an obvious path forward. OBJECTIVE: We aimed to understand the current digital landscape and enabling environment around respiratory health to reduce costs, avoid duplication, and understand the comprehensiveness of DHIs. METHODS: This study will follow a scoping review methodology as outlined by Arksey and O'Malley, the Joanna Briggs Institute, and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, PakiMedNet, and MyMedR databases will be searched along with key websites, repositories, and gray literature databases. The terms "respiratory health," "digital health," "South Asia," and "Southeast Asia," as well as related terms will be searched. The results will be screened for duplicates and then against the inclusion and exclusion criteria. For the studies included, data will be extracted, collated, and analyzed. RESULTS: The scoping review was started in July 2023 and will be finalized by February 2024. Results will be presented following the World Health Organization's classification of DHIs to categorize interventions in a standardized format and the mobile health evidence reporting and assessment checklist to report on the effectiveness of interventions. Further exposition of the evidence extracted will be presented through narrative synthesis. CONCLUSIONS: As DHIs continue to proliferate, the need to understand the current landscape becomes more pertinent. In this scoping review, we will seek to more clearly understand what digital health tools and technologies are being used in the current landscape of digital health in South and Southeast Asia for respiratory health and to what extent they are addressing the respiratory health needs of the region. The results will inform recommendations on digital health tools for respiratory health in South and Southeast Asia will help funders and implementers of DHIs leverage existing technologies and accelerate innovations that address documented gaps in the studied countries. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52517.
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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 | 0.109 | 0.093 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.020 |
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".