Prenatal Breastfeeding Education: A Health Equity Approach
Bibliographic record
Abstract
Breastfeeding has a direct impact on the health of individuals and communities. Despite the implementation of diverse health promotion and education strategies, global breastfeeding rates remain relatively low. Predominantly, these strategies are implemented during the prenatal period and focus on individual-level education and behavior change, with less attention paid to the structural and contextual resources necessary for breastfeeding support and education. Breastfeeding experiences are contextually shaped by social, cultural, economic, and political factors. In addition, breastfeeding education, support, and rates are influenced by complex structures such as policy. Thus, a health equity approach to guide the planning and implementation of prenatal breastfeeding education can contribute to improving breastfeeding rates for diverse individuals and populations. Drawing from critical theoretical and health equity concepts, we present an equity approach with the potential for direct application to educating parents about breastfeeding. We discuss how a health equity approach could guide policymakers and perinatal healthcare providers to ensure culturally, emotionally, and psychologically safe learning spaces to reduce access barriers to breastfeeding education. Also, the approach could tailor the learning of parents to accommodate the unique contexts of their lives, thereby optimizing their breastfeeding experiences and outcomes.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".