Poverty and Breastfeeding: Comparing Determinants of Early Breastfeeding Cessation Incidence in Socioeconomically Marginalized and Privileged Populations in the FiNaL Study
Bibliographic record
Abstract
Purpose: Infant feeding differences are strongly tied to socioeconomic status. The goal of this study is to compare determinants of early breastfeeding cessation incidence in socioeconomically marginalized (SEM) and socioeconomically privileged (SEP) populations, focusing on birthing parents who intended to breastfeed. Methods: This cohort study includes data from 451 birthing parents in the Canadian province of Newfoundland and Labrador who reported intention to breastfeed in the baseline prenatal survey. Multivariate logistic regression techniques were used to assess the determinants of breastfeeding cessation at 1 month in both SEM and SEP populations. Results: The analysis data included 73 SEM and 378 SEP birthing parents who reported intention to breastfeed at baseline. At 1 month, 24.7% (18/73) in the SEM group had ceased breastfeeding compared to 6.9% (26/378) in the SEP group. In the SEP population, score on the Iowa Infant Feeding Attitude Scale (IIFAS) (odds ratio [OR] 3.33, p=0.01) was the sole significant determinant. In the SEM population, three significant determinants were identified: unpartnered marital status (OR 5.10, p=0.05), <1 h of skin-to-skin contact after birth (OR 11.92, p=0.02), and negative first impression of breastfeeding (OR 11.07, p=0.01). Conclusion: These results indicate that determinants of breastfeeding cessation differ between SEM and SEP populations intending to breastfeed. Interventions intended on improving the SEM population's postpartum breastfeeding experience using best practices, increasing support, and ensuring at least 1 h of skin–skin contact may increase breastfeeding rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".