End User Experience of Hayat App and Dashboard: A Qualitative Assessment of Usability, Community Engagement, and Validity of Data for Antenatal Care Provision And Routine Immunization in Rural Pakistan And Afghanistan
Bibliographic record
Abstract
Abstract Introduction Pakistan and Afghanistan have an extensive network of community health workers (CHWs) who provide counseling to rural households on basic maternal and child care (MCH), report household service coverage, and provide referrals to health centers. An android-based mobile health application for maternal and child health was piloted in underserved remote areas within Northern Pakistan and bordering Bamyan and Badakshan provinces of Afghanistan to enable community health workers in Afghanistan and both community health workers and vaccinators in Pakistan, to report real-time data on outreach visits as well as immunization and maternity health coverage of eligible clients. A qualitative assessment of health worker experience with the Mobile App was carried out as part of the end-line assessment of the pilot. Objective The objective was to examine the end-user perceptions of the usability of the digital application data, community acceptability of the data, and use of data supervision and management decisions. The purpose was to identify barriers and enablers to inform the integration of the mhealth application for reporting by community health workers within the district health systems in an LMIC setting. Methods Primary data was collected through focus group discussions with frontline health workers and key informant interviews with field supervisors as well as sub-national managers. Seventeen focus group discussions were carried out within purposely selected study catchment sites. These included 9 FGDs with community-based Lady Health Workers (LHWs), LHW supervisors, and vaccinators in Northern Pakistan; and 8 FGDs with Community Health Workers (CHWs) and CHW supervisors. Additionally, 28 key informant interviews were carried out with field supervisors, immunization, and MCH managers at the district and provincial levels. Deductive thematic content analysis was undertaken based on an adapted framework from the World Health Organization guide for “Monitoring and Evaluating Digital Health Interventions” and the Technology Acceptance Model (TAM). Findings Frontline health workers perceived the application to be highly usable and the use of Android phones for reporting was reported to be acceptable to the communities as long as photographic evidence was not collected. Increased workload due to both paper and digital reporting, occasional connectivity issues, and security issues with the use of mobile phones in certain areas were key primary barriers, whereas low motivation and increasing task load of frontline health workers were secondary issues reported. Supervisors and health managers perceived an improvement in the timeliness of data reporting by frontline health workers as well as more complete reporting. The app-collected data was perceived to facilitate data verification on the ground and managers were more confident of the reliability of digital reporting as compared to paper-based records. Conclusion: The use of the smartphone-based application has good acceptability among frontline health workers and their managers and was perceived to provide more reliable data timely data as compared to paper-based reporting benefits. The duplicative paper-based system, security in remote areas, and chronic issues with health worker programs are challenges that need to be encountered for embedding within the health system.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".