Development and Usability of the OHiFamily Mobile App to Enhance Accessibility to Maternal and Infant Information for Expectant Families in Ohio: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The Infant Mortality Research Partnership in Ohio is working to help pregnant individuals and families on Medicaid who are at risk for infant mortality and preterm birth. As part of this initiative, researchers at The Ohio State University worked to develop a patient-facing mobile app, OHiFamily, targeted toward, and created for, this population. To address the social determinants of health that can affect maternal and infant health, the app provides curated information on community resources, health care services, and educational materials in a format that is easily accessible and intended to facilitate contact between families and resources. The OHiFamily app includes 3 distinct features, that is, infant care logging (eg, feeding and diaper changes), curated educational resources, and a link to the curated Ohio resource database (CORD). This paper describes the development and assessment of the OHiFamily app as well as CORD. OBJECTIVE: This study aimed to describe the development of the OHiFamily mobile app and CORD and the qualitative feedback received by the app's intended audience. METHODS: The researchers performed a landscape analysis and held focus groups to determine the resources and app features of interest to Ohio families on Medicaid. RESULTS: Participants from several focus groups were interested in an app that could offer community resources with contact information, information about medical providers and information and ways to contact them, health tips, and information about pregnancy and infant development. Feedback was provided by 9 participants through 3 focus group sessions. Using this feedback, the team created a curated resource database and mobile app to help users locate and access resources, as well as access education materials and infant tracking features. CONCLUSIONS: OHiFamily offers a unique combination of features and access to local resources for families on Medicaid in Ohio not seen in other smartphone apps.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".