ABA‐Feed Infant Feeding Training for Peer Supporters and Coordinators: Development and Mixed‐Methods Evaluation
Bibliographic record
Abstract
The assets-based feeding help before and after birth (ABA-feed) intervention aims to improve breastfeeding rates by offering proactive peer support to first-time mothers, regardless of feeding intention. Based on behaviour change theory and an assets-based approach, the intervention involved training existing peer supporters to become infant feeding helpers (IFHs). A train-the-trainer model was used, with coordinators delivering four 2-h training sessions to IFHs. Training covered a study overview, IFH role, role-play scenarios and signposting to local assets. Due to COVID-19, training was delivered online. Post-training questionnaires were completed by 22/30 (73.3%) coordinators and 119/193 (61.7%) IFHs, and qualitative interviews were conducted with 24 coordinators and 72 IFHs. Researchers observed training at five sites, assessing fidelity, engagement and delivery quality. Questionnaire data were analysed descriptively, and qualitative data were analysed using framework analysis. Findings indicated that coordinators valued the train-the-trainer model, particularly information on formula feeding and antenatal discussions. IFHs found training engaging and felt prepared, though some were apprehensive about formula feeding support. While online training was convenient, challenges included monitoring discussions and role-play in breakout rooms. Most participants favoured a hybrid approach, with in-person sessions for interactive activities. Observations showed high training fidelity, participant engagement and confidence in delivering intervention components. The ABA-feed training was acceptable to coordinators and IFHs and was delivered with fidelity. Future training should adopt a hybrid approach, incorporating diverse resources and prioritising in-person interactive components such as role-play. Trial Registration: ISRCTN17395671.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".