Knowledge, attitudes and perceptions on infant and young child nutrition and feeding among adolescent girls and young mothers in rural <scp>B</scp>angladesh
Bibliographic record
Abstract
Improved infant and young child feeding (IYCF) practices have the potential to improve child health and development outcomes in poorly resourced communities. In Bangladesh, approximately 60% of rural girls become mothers before the age of 18, but most interventions to improve IYCF practices target older mothers. We investigated the knowledge, attitudes and perceptions regarding IYCF among adolescent girls and young women aged 15-23 years old in two rural regions in north-west Bangladesh and identified the main points of concordance with, or mismatch to, key international IYCF recommendations. We compared qualitative data collected during interviews and focus groups with participants who were unmarried, married without a child and married with at least one child, and stratified by region. Qualitative indicators of concordance with international recommendations suggest that IYCF knowledge of participants was limited, irrespective of marriage or maternity. Young mothers in our study were no more knowledgeable about feeding practices than their nulliparous peers. Some participants were well aware of an IYCF recommendation (e.g. to exclusively breastfeed for 6 months), but their interpretation of the recommendation deviated from the intended public health message. Notions of insufficient or 'spoiled' breast milk, gender-based biases in feeding intentions and understandings of infant needs, and generational shifts in feeding practices were commonly reported. Conclusions are that female adolescence is a window of opportunity for improving health outcomes among future children, and increased investment in early education of adolescent girls regarding safe IYCF may be an effective strategy to promote and support improved infant feeding practices.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".