Quality of Mobile Apps for Child Development Support: Search in App Stores and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Following increases in smartphone access, more parents seek parenting advice through internet sources, including blogs, web-based forums, or mobile apps. However, identifying quality apps (ones that respond to the diverse experiences of families) for guidance on child development can be challenging. OBJECTIVE: This review of mobile health apps aimed to document the landscape, design, and content of apps in the United States available to parents as they promote their child's developmental health. METHODS: To understand the availability and quality of apps for early childhood health promotion, we completed a content analysis of apps in 2 major app stores (Google Play and Apple App stores). RESULTS: We found that most apps do not provide tailored experiences to parents, including cultural considerations, and instead promote generic guidance that may be useful to parents in some contexts. We discuss the need for an evaluative framework to assess apps aimed to support parents on child development topics. CONCLUSIONS: Future work is needed on how to support designers in this area, specifically related to avoiding potential burdens on users and providing culturally informed and equity-driven experiences.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".