Experiences of the ABA‐Feed Infant Feeding Intervention: A Qualitative Study With Women, Peer Supporters and Coordinators
Bibliographic record
Abstract
UK breastfeeding rates are low, with health inequalities in initiation and continuation. Breastfeeding peer support interventions are recommended in UK and global policy. The Assets-based feeding help Before and After birth (ABA-feed) trial tested the effectiveness of proactive, woman-centred support for infant feeding delivered by trained peer supporters (infant feeding helpers; IFHs) in addition to usual care at 17 UK sites. Using data from an embedded process evaluation, this paper reports the views and experiences of women receiving, and the IFHs and coordinators delivering, ABA-feed. Women (n = 2475) were recruited to the trial antenatally; 1458 were allocated to the intervention. Thirty women from five study sites took part in qualitative interviews between 9 and 23 weeks postnatal. IFHs (n = 72) and coordinators (n = 25) from across all sites participated in individual or group interviews towards the end of the intervention period. Interview transcripts were analysed alongside 1147 free-text responses from an 8-week postnatal follow-up survey using Framework Analysis. The ABA-feed intervention was highly acceptable to women, including younger women, those with less education, from diverse ethnic groups, single mothers, and those who intended to formula feed, as well as to IFHs and coordinators. Both remote and in-person support was acceptable. While women valued proactive daily contact during the first 14 days postpartum, some IFHs found this challenging, and some struggled with supporting women who chose formula feeding or were less engaged. This study highlights the value of flexible, proactive, woman-centred infant feeding support. TRIAL REGISTRATION: ISRCTN17395671.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".