Attitudes, beliefs and social norms regarding infant and young child feeding among Nigerian mothers, fathers and grandmothers across time
Bibliographic record
Abstract
Infant and young child feeding (IYCF) interventions in low-resource countries mainly target pregnant women and mothers of young children; however, fathers and grandmothers also influence IYCF practices. We conducted focus group discussions with mothers, fathers and grandmothers of young children across three time points in areas where an IYCF social and behaviour change intervention was implemented in Nigeria to explore differences by participant type and shifts over time in attitudes, beliefs and social norms related to breastfeeding and dietary diversity (DD). Overall, across time points, we found more discrepancies in attitudes, beliefs and social norms for early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) among different participant types than for DD. Although most participants agreed EIBF and EBF are good practices, mothers believed this more strongly than fathers and grandmothers; however, at endline, a shift towards acceptance of EIBF and EBF appeared among fathers and grandmothers. Across time points, all participant types acknowledged the nutritional and health benefits of green leafy vegetables and animal-source foods but described various barriers to feeding them to children. Across time points, all participant types also highlighted the importance of health workers and antenatal visits as important sources of IYCF knowledge and facilitators to following recommended practices. Insights from this study highlight the importance of including key influencers of IYCF practices in qualitative research.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".