Patterns in Mothers’ Recollection of Health Care Providers’ Young Child Feeding Recommendations
Bibliographic record
Abstract
OBJECTIVE: Characterize feeding guidance mothers recall receiving from their child's health care provider (HCP). DESIGN: Cross-sectional study of mothers participating in the 2017-2019 National Survey of Family Growth. PARTICIPANTS: US mothers reporting (n = 1,302) information about their youngest child (aged 6 months to 5 years). VARIABLES MEASURED: Weighted percentage of mothers who recalled their child's HCP discussing 6 different feeding topics by demographic characteristics. ANALYSIS: Logistic regression assessing the relationship between recall of feeding guidance and demographics. RESULTS: In this sample, 36.9% of mothers (95% confidence interval, 32.3-41.4) recalled HCPs recommending solid food before 6 months old (34.6% at 4-5 months, and 2.3% before 4 months). Mothers who were older or had a higher education level were more likely than their counterparts to recall their HCP discussing several of the feeding topics examined. CONCLUSION AND IMPLICATIONS: Mothers reported high recollection of early childhood nutrition guidance from their HCP; however, certain topics (eg, appropriate timing of solid food introduction) could be prioritized, and some subpopulations may need additional focus to improve receipt of messages. A better understanding of variability in recall of feeding guidance could provide information for interventions to address barriers to receiving and retaining guidance.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".