A Mobile App to Promote Breastfeeding Self-Efficacy in Preterm Infants’ Mothers: Development and Validation
Bibliographic record
Abstract
Improving breastfeeding rates is a global goal. To achieve it, actions targeting modifiable factors that influence the breastfeeding experience, such as maternal self-efficacy, could be a promising path, especially with preterm infants' mothers. Considering the current ubiquitous technology, we developed a mobile application for mothers of preterm infants to constitute a breastfeeding information and support platform. The study was developed in three phases: a survey to determine characteristics and preferences of preterm infants' mothers; the app development by an interdisciplinary team, following the principles of Disciplined Agile Delivery; and the face and content validation by 10 professionals. The app contains 80 screens and 11 strategies to address prematurity, lactation, breastfeeding, peer support, maternal emotions, resilience, and motivation. Nurses can apply their expertise by designing mHealth-based interventions, employing scientific evidence, and considering the interests and preferences of the target population. Future studies will assess the user experience, the effect on breastfeeding self-efficacy, and breastfeeding rates, and develop a culturally adapted English version of the app for women in Canada.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".