A Mobile App to Promote Breastfeeding Self-Efficacy in Preterm Infants’ Mothers: Development and Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.002 |
| 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 | 0.001 |
| 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 it