Feasibility of Real-Time Mobile Phone Case Notification by Village Malaria Workers in Rural Myanmar: A Mixed Methods Study
Bibliographic record
Abstract
Malaria burden has markedly decreased in Myanmar and is on course for elimination by 2030. Interrupting of local transmission is essential, and timely notification within 24 hours of disease occurrence by frontline village malaria workers (VMWs) is a crucial initial component of timely follow-up by response teams. Here we studied the feasibility of real-time case notification using mobile phones among VMWs in the remote Banmauk Township, Sagaing Region, Myanmar. A structured quantitative and qualitative questionnaire was used for data collection after implementing the intervention for six months between May and October 2018. Ten VMWs from the National Malaria Control Programme (NMCP) in ten scattered villages from the township were randomly recruited and given one day of on-site training on reporting methods and how to use their own mobile phone. VMWs received 5,000MMK (approximately 3USD) per month remuneration. The baseline demographics of VMWs were not significantly different. Twenty-four out of 25 (96%) malaria patients were notified within 24 hours by the ten VMWs during the study period. All submitted information were said to be complete and correct. VMWs suggested the system as simple and acceptable despite some challenges. In the qualitative study, almost all VMWs were satisfied with the system and willing to use it in the future. This mobile phone reporting system is more efficient and easier to use than other more complicated online mobile applications. However, only a few indicators can be submitted using this approach and the system cannot be used in areas without network coverage.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".