Women's and peer supporters' experiences of an assets‐based peer support intervention for increasing breastfeeding initiation and continuation: A qualitative study
Bibliographic record
Abstract
BACKGROUND AND CONTEXT: Breastfeeding peer support is valued by women, but UK trials have not demonstrated efficacy. The ABA feasibility trial offered proactive peer support underpinned by behaviour change theory and an assets-based approach to women having their first baby, regardless of feeding intention. This paper explores women's and infant feeding helpers' (IFHs) views of the different components of the ABA intervention. SETTING AND PARTICIPANTS: Trained IFHs offered 50 women an antenatal meeting to discuss infant feeding and identify community assets in two English sites-one with a paid peer support service and the other volunteer-led. Postnatally, daily contact was offered for the first 2 weeks, followed by less frequent contact until 5 months. METHODS: Interviews with 21 women and focus groups/interviews with 13 IFHs were analysed using thematic and framework methods. RESULTS: Five themes are reported highlighting that women talked positively about the antenatal meeting, mapping their network of support, receiving proactive contact from their IFH, keeping in touch using text messaging and access to local groups. The face-to-face antenatal visit facilitated regular text-based communication both in pregnancy and in the early weeks after birth. Volunteer IFHs were supportive of and enthusiastic about the intervention, whereas some of the paid IFHs disliked some intervention components and struggled with the distances to travel to participants. CONCLUSIONS: This proactive community assets-based approach with a woman-centred focus was acceptable to women and IFHs and is a promising intervention warranting further research as to its effect on infant feeding outcomes.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".