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
Abstract Background Mobile health (mHealth) technologies are 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 mHealth application in enhancing immunization service delivery and data accuracy compared with traditional manual Expanded Programme on Immunization (EPI) registers in upper and lower Chitral, Khyber Pakhtunkhwa (KPK), Pakistan. Methodology This cross-sectional study evaluated the usability and accuracy of the Hayat mHealth application for vaccination data recording. From a sampling frame of 63 public and private healthcare facilities across five union councils in upper and lower Chitral, 17 were randomly selected and assessed by the KPK provincial EPI team. Vaccinators’ performance was assessed through structured questionnaire, and vaccination data from January to March 2024 were compared between the Hayat mHealth application and manual daily registers using a predefined 10% discrepancy threshold. Field verification using vaccination cards further validated records in the Hayat application. Frequencies and percentages summarized vaccinators’ proficiency, and percentage differences (delta) between sources for each antigen were reported with 95% confidence intervals. Results The cumulative percentage difference (delta) between Hayat and manual records remained within the acceptable 10% threshold for most vaccine antigens, indicating good data accuracy. All 19 participating vaccinators demonstrated proficiency in core application features, including registration and child search; however, gaps were identified in advanced functions such as data integration and offline data sharing. Vaccinator satisfaction with the application was high. 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). Conclusion The Hayat mHealth application demonstrated feasibility and usability for improving immunization data accuracy and service delivery in remote and resource-limited settings. Clinical trial number Not applicable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".