Nurture Early for Optimal Nutrition (NEON) Pilot Randomised Controlled Trial: Qualitative study of community facilitators and attendees’ perspective on intervention delivery
Bibliographic record
Abstract
Abstract Background Appropriate and healthy feeding practices can enhance a child’s health, prevent obesity, and reduce chronic metabolic disease risks. Given the ethnic variations in feeding practices and metabolic risk, interventions must be community specific. Culturally tailored, grassroots interventions targeting infant feeding can induce behavioural changes, mitigating chronic metabolic disease risks in later life. Aim The aim of this study was to explore participant feedback and inform intervention delivery methods within marginalised communities. Methods A pilot three-arm cluster randomised controlled trial was conducted in London’s Tower Hamlets and Newham boroughs, involving community participatory learning and action groups. The study recruited 186 South Asian (Indian, Bangladeshi, Pakistani, and Sri Lankan) mothers or carers of 0-2-year-old children. Attendees were invited to either face-to-face or online intervention arms, facilitated by trained multilingual community facilitators, offering culturally informed discussions on child nutrition and care practices. Qualitative feedback was collected from attendees and facilitators, with thematic analysis identifying key themes, underscoring intervention fidelity and acceptance. Results Of the initial attendees, 42 (from the remaining 153 at the study’s conclusion) and 9 community facilitators offered feedback on the intervention’s delivery and suggestions for enhancing community-based interventions’ success. Key findings highlighted the need for a more flexible approach to boost participation and the significance of providing accessible, translated documents and resources. Conclusion Parenting interventions, particularly for new mothers, should adopt a hybrid design. This would provide attendees with the flexibility to select the delivery method, session timings, and the option to participate at any stage of the intervention.
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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 | 0.056 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".