Equipping community health workers with digital tools for pandemic response in LMICs
Bibliographic record
Abstract
BACKGROUND: Community health workers (CHWs) are well-positioned to play a pivotal role in fighting the pandemic at the community level. The Covid-19 outbreak has led to a lot of stress and anxiety among CHWs as they are expected to perform pandemic related tasks along with the delivery of essential healthcare services. In addition, movement restrictions, lockdowns, social distancing, and lack of protective gear have significantly affected CHWs' routine workflow and performance. To optimize CHWs' functioning, there is a renewed interest in supporting CHWs with digital technology to ensure an appropriate pandemic response. DISCUSSION: The current situation has necessitated the use of digital tools for the delivery of Covid-19 related tasks and other essential healthcare services at the community level. Evidence suggests that there has been a significant digital transformation to support CHWs in these critical times such as remote data collection and health assessments, the use of short message service and voice message for health education, use of digital megaphones for encouraging behavior change, and digital contract tracing. A few LMICs such as Uganda and Ethiopia have been successful in operationalizing digital tools to optimize CHWs' functioning for Covid-19 tasks and other essential health services. CONCLUSION: Yet, in most LMICs, there are some challenges concerning the feasibility and acceptability of using digital tools for CHWs during the Covid-19 pandemic. In most cases, CHWs find it difficult to adopt and use digital health solutions due to lack of training on new digital tools, weak technical support, issues of internet connectivity, and other administrative related challenges. To address these challenges, engaging governments would be essential for training CHWs on user-friendly digital health solutions to improve routine workflow of CHWs during the Covid-19 pandemic.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".