Investigating Maternal Perspectives of Breastfeeding Support Targeted Towards Fathers in the Milk Man Mobile App Intervention
Bibliographic record
Abstract
BACKGROUND: The support of her infant's father is one of the most important factors influencing a mother's breastfeeding success, and an increasing number of interventions are targeted towards fathers. Engaging fathers as agents to influence a maternal behavior is potentially problematic, yet few studies report on maternal experiences. OBJECTIVE: This study aims to explore mothers' perspectives of their partners' use of Milk Man, a father-focused breastfeeding smartphone app, and the acceptability of this approach. MATERIALS AND METHODS: New mothers (N = 459) whose partners had access to the app completed a questionnaire at six weeks postpartum. These data were used to determine knowledge, use and perspectives of the app. A sentiment analysis was conducted on responses to an open-ended question seeking maternal perspectives of the app. RESULTS: Just over a quarter of mothers (28%) had been shown something from the app, and 37% had discussed something from Milk Man with their partner. There were 162 open-ended responses related to mothers' perspectives of the app. Relevant responses (n = 129) were coded to an overall sentiment node and then to a total of 23 child nodes (sub-nodes). Most comments were positive (94), with a smaller number either negative (25) or neutral (21). Negative comments related to the usability of the app and not its intent or content. CONCLUSION: Mothers found the father-focussed breastfeeding app to be acceptable. When designing interventions targeting one group to affect the behaviour of another, inclusion of measures to gain the perspectives of both should be seen as an imperative.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".