Features and Educational Content Related to Milk Production in Breastfeeding Apps: Content Analysis Informed by Social Cognitive Theory
Bibliographic record
Abstract
BACKGROUND: Low milk production is one of the main reasons for premature breastfeeding cessation. Smartphone apps have the potential to assist mothers with promoting, interpreting, tracking, or learning about milk production. It is not known whether breastfeeding apps contain high-quality, engaging, and diverse content and features that could be used by mothers to increase their breastfeeding self-efficacy and answer their questions about milk production. OBJECTIVE: The overarching objective of this study was to identify and evaluate features and content within breastfeeding apps that could be used by mothers to increase breastfeeding self-efficacy and answer their questions about milk production. The secondary objectives were to quantify the diversity of representation of breastfeeding experiences within breastfeeding apps and to define the type of organization that is most likely to create free apps and/or apps with high-quality, engaging, and diverse features and content related to milk production. METHODS: Breastfeeding apps were identified in the Apple App Store. All features that assist mothers with tracking, promoting, or interpreting milk production in the first 0-6 months postpartum were noted. Every screen containing educational information about milk production was identified and saved for review. Images of mothers and caretakers within the selected screenshots were assessed. Three scoresheets informed by Social Cognitive Theory were created to evaluate all identified features, educational content, and images representing the breastfeeding experience. RESULTS: Forty-one breastfeeding apps were reviewed. Only seven apps contained both features and educational content related to milk production. Thirteen apps that contained educational content related to milk production received a mean combined content and cultural diversity score of 15.3 of 78. Of the 48 photos reviewed in screenshots that contained educational content on milk production, 87.5% (n=42) were of white women and their infants. For-profit companies and large organizations were most likely to create free apps and apps that received high scores on the combined content and diversity or features scoresheet, respectively. CONCLUSIONS: Features and educational content related to milk production and breastfeeding imagery within breastfeeding apps were evaluated using three novel scoresheets informed by Social Cognitive Theory. Few apps contained both features that promote breastfeeding self-efficacy and high-quality, engaging, educational content with images of diverse caretakers. Thus, it is likely that parents, especially those from minority or low-income groups, have limited options when selecting a breastfeeding app. App developers could use the scoresheets and findings in this review to develop breastfeeding apps that assist mothers with interpreting, tracking, or learning about milk production through high-quality and engaging features, content, and imagery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".