Effectiveness of an Educational Manual to Promote Infant Feeding Practices in Primary Health Care
Bibliographic record
Abstract
Objective: To test the hypothesis that a continuing educational strategy (ie, “the manual”) in primary health-care improves infant feeding practices among infants under 1 year of age. Methods: A before and after study was conducted at primary health-care units in Embu das Artes, Brazil. The intervention was the use of a manual created to support continuing educational activities on breastfeeding and complementary feeding to be performed by tutors of Estratégia Amamenta e Alimenta Brasil with health-care teams, in a period of 8 months. Five hundred sixty-one mothers before and 598 mothers after intervention were interviewed about breastfeeding and complementary feeding practices. Multivariate analysis was performed using Poisson multilevel regression to test the hypothesis. Results: Lack of minimum food diversity (before 62.9%; after 50.3%) and lack of food adequacy (before 77.5%; after 63.3%) decreased significantly. Regression analysis confirmed that infants after the intervention had lower prevalence of inadequacy of complementary feeding. While the intervention did not show significant association with exclusive breastfeeding, it showed association with the improvement of complementary feeding practices. Conclusions: The manual is a continuing educational strategy that improved complementary feeding practices in primary health care.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".