Strengthening Expanded Program for Immunization Service Delivery through the Hayat mHealth Application: A Cross-Sectional Study in Upper and Lower Chitral, Pakistan
Bibliographic record
Abstract
<title>Abstract</title> <bold>Background</bold> Mobile health (mhealth) technologies are revolutionizing and transforming healthcare delivery, particularly in low resource settings, by improving data accuracy, accessibility and decision making. This study aimed to evaluate the effectiveness and usability of the Hayat m-health application in enhancing immunization service delivery ae well as data accuracy, compared to traditional manual EPI registers in Upper and Lower Chitral, Khyber Pakhtunkhwa KPK, Pakistan.<bold>Methodology</bold> A cross-sectional study conducted across 63 healthcare facilities using structured tools and data comparisons from the month of January to March 2024.<bold>Results</bold> The results showed that cumulative percentage difference (delta) between Hayat and manual records remained within acceptable 10% threshold for most vaccine antigens, indicating strong data accuracy. Whereas 100% of the 19 participating vaccinators demonstrated proficiency in features including registration and child search, although gaps were identified in advanced functions, like data integration and offline sharing. Vaccinator satisfaction with the application was undisputed. The matching of record between Hayat Application and vaccination cards exceeded 95% for all the essential antigens (BCG, Penta 1, Penta 2, Penta 3, MR 1, and MR 2). Therefore, these results support Hayat Application potential for improving immunization data accuracy and service delivery in the remote areas.<bold>Conclusion</bold> The Hayat mhealth application demonstrated strong potential for improving immunization of data accuracy and service delivery in remote areas and resource limited settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".