Operability, Acceptability, and Usefulness of a Mobile App to Track Routine Immunization Performance in Rural Pakistan: Interview Study Among Vaccinators and Key Informants
Bibliographic record
Abstract
BACKGROUND: There has been a recent spate of mobile health (mHealth) app use for immunizations and other public health concerns in low- and middle-income countries. However, recent evidence has largely focused on app development or before-and-after effects on awareness or service coverage. There is little evidence on the factors that facilitate adoption of mHealth programs, which is critical to effectively embed digital technology into mainstream health systems. OBJECTIVE: This study aimed to provide the qualitative experiences of frontline health staff and district managers while engaging with real-time digital technology to improve the coverage of routine childhood immunization in an underserved rural district in Pakistan. METHODS: An Android-based app was iteratively developed and used for a 2-year period in 11 union councils of the Tando Muhammad Khan district, an underserved rural district with poor immunization coverage in Pakistan. We used iterative methods to examine the (1) acceptability and operability of the app, (2) validity of the collected data, and (3) use of the collected data. In addition, we collected the barriers and enablers for uptake of the mHealth app. Each of these topics was further explored related to changes in work as well as the enabling factors for and barriers to app use. In-depth interviews were conducted with the 26 vaccinators posted in the 11 union councils and 7 purposively selected key informants (government district managers) involved with the Expanded Program for Immunization. Findings were triangulated in line with the three broad research areas. RESULTS: Digital immunization tracking was considered acceptable by vaccinators and district managers. Real-time immunization data were used to monitor vaccination volume, track children with incomplete vaccinations, develop outreach visit plans, correct existing microplans, and disburse a fuel allowance for outreach sessions. The validity of the app data was perceived to be superior to that of data from manual records. Ease of operability, satisfaction with data, personal recognition, links to field support, and a sense of empowerment served as powerful enablers. Taking twice the time to complete both manual and digital entries and outdated phones over time were considered constraints. An unintended knock-on effect was improved coordination and strengthening of Expanded Program for Immunization review platforms across district stakeholders through digitalized data. CONCLUSIONS: Embedding digital technology into mainstream health systems relies on use by both end users and district stakeholders. Ease of operability, satisfaction with data reliability, personal recognition, links to field support, and empowerment are powerful enablers, whereas improved coordination as a result of easy, transparent data access can be an important by-product of digitalization. Findings are relevant not only for wide-scale implementation of immunization tracking apps in Pakistan but also for informing the use of digital technology for results-based delivery by frontline health workers.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".