Iterative Development of a Mobile Phone App to Support Community Health Volunteers During Cervical Cancer Screening in Western Kenya: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: To achieve the World Health Organization targets for cervical cancer elimination, low- and middle-income countries will need to develop innovative strategies to provide human papillomavirus (HPV)-based screening at a population level. Although mobile health (mHealth) interventions may help realize these goals by filling gaps in electronic specimen tracking and patient education, effective implementation of mHealth interventions is dependent upon context-specific development that is acceptable and usable by the target population. Detailed feedback should be gathered at the design and development stages to yield final products that reflect the needs, desires, and capabilities of target users. OBJECTIVE: The aim of this study is to develop an mHealth app (mSaada) to support HPV-based screening in partnership with community health volunteers (CHVs) and program planners in western Kenya. METHODS: A team of student programmers developed a prototype to meet previously identified gaps in screening: patient education, protocol support, data capture, and specimen tracking. The prototype was iteratively developed through 2 waves of in-person working sessions with quantitative (survey) and qualitative (in-depth interview) feedback. Research staff engaged key stakeholders from both urban and rural locations and with varying levels of experience in delivering screening services. During the sessions, participants completed simulation exercises and role-play activities to become familiar with the platform. Once feedback was gathered and synthesized after each wave of in-person data collection, developers implemented changes to improve mSaada functionality. RESULTS: A total of 18 CHVs and clinicians participated in the in-person sessions. Participants found mSaada useful, easy to use, and would meet the needs of CHVs to provide HPV-based cervical cancer screening (electronic data capture, client education resources, and specimen tracking). They provided key feedback to enhance user experience, workflow, and sustainability. Key changes included altering the appearance of the wireframes, adding translation in additional local languages, changing potentially insensitive figures, alphabetizing lengthy dropdown menus, adding clinically relevant logic checks when entering data, and incorporating the ability to make real time edits to client records. They also made recommendations for additional features that might enhance mSaada's impact at the facility and health system levels, specifically the inclusion of a report-generating tool consistent with the Ministry of Health standards. CONCLUSIONS: Using a process of iterative feedback with key stakeholders and rapid response from developers, we have developed a mobile app ready for pilot testing in HPV-based screening programs led by CHVs.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".