Effectiveness of an educational manual to promote infant feeding practices in primary health care
Bibliographic record
Abstract
Abstract Background Breastfeeding and adequate complementary feeding in the first years of life provide short- and long-term benefits for child health. However, breastfeeding and adequate complementary feeding prevalence's are low. In Brazil, the Estratégia Amamenta e Alimenta Brasil (EAAB) aims to improve the quality of the assistance provided by primary healthcare teams to ultimately improving infant feeding practices in the Brazilian population. In primary healthcare units, continuing education in breastfeeding and complementary feeding is facilitated by EAAB tutors. A Manual to Support EAAB Tutor (the Manual) was developed following a problem-based learning methodology. This study aimed to evaluate the effectiveness of using the Manual by analyzing exclusive breastfeeding and complementary feeding indicators. Methods A before and after study was conducted at primary healthcare units in Embu das Artes, Brazil. The intervention consisted in the use of the Manual by EAAB tutors who performed some comprehensive training activities with healthcare teams, in a period of 8 months. Subjects consisted of mothers of infants under one year of age attended at primary healthcare units. 561 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. Key messages The use of a continuing education strategy grounded on a problem-based learning methodology was effective to produce improvements in complementary feeding practices in primary health care. The 'Manual to Support EAAB Tutor' consists of a tool that has the potential to be adapted for different contexts and easy to disclose to all EAAB tutors in the country.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".