Exploring digital health interventions to support community health workers in low-and-middle-income countries during the COVID-19 pandemic: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: COVID-19 has significantly affected community health workers' (CHWs) performance as they are expected to perform pandemic-related tasks along with routine essential healthcare services. A plausible way to optimise CHWs' functioning during this pandemic is to couple the efforts of CHWs with digital tools. So far, no systematic evidence is available on the use of digital health interventions to support CHWs in low-middle-income countries (LMICs) amid the COVID-19 pandemic. The article describes a protocol for a scoping review of primary research studies that aim to map evidence on the use of unique digital health interventions to support CHWs during COVID-19 in LMICs. METHODS AND ANALYSIS: and the Joanna Briggs Institute. Our search strategy has been developed for the following four main electronic databases: Excerpta Medica Database, Medical Literature Analysis and Retrieval System Online, Cochrane Central Register of Controlled Trials and Cumulated Index to Nursing and Allied Health Literature. Google Scholar and reference tracking will be used for supplementary searches. Each article will be screened against eligibility criteria by two independent researchers at the title and abstract and full-text level. The review will include studies that targeted digital health interventions at CHWs' level to provide support in delivering COVID-19-related and other essential healthcare services. A date limit of 31 December 2019 to the present date will be placed on the search and English language articles will be included. ETHICS AND DISSEMINATION: Formal ethical approval is not required, as primary data will not be collected in this study. The results from our scoping review will provide valuable insight into the use of digital health interventions to optimise CHWs' functioning and will reveal current knowledge gaps in research. The results will be disseminated through journal publications and conference presentations.
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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 | 0.067 | 0.066 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".