Birth Experiences, Breastfeeding, and the Mother-Child Relationship: Evidence from a Large Sample of Mothers
Bibliographic record
Abstract
BACKGROUND: It is a priority for public health professionals to improve global breastfeeding rates, which have remained low in Western countries for more than a decade. Few researchers have addressed how maternal perceptions of birth experiences affect infant feeding methods. Furthermore, mixed results have been shown in research regarding breastfeeding and mother-child bonding, and many studies are limited by small sample sizes, representing a need for further investigation. PURPOSE: We aimed to examine the relationship between subjective birth experiences and breastfeeding outcomes, and explored whether breastfeeding affected mother-infant bonding. METHODS: 3,080 mothers up to three years postpartum completed a cross - sectional survey. RESULTS: Mothers who had more positive birth experiences were more likely to report breastfeeding their babies. Moreover, mothers who perceived their birth as more positive were more likely to breastfeed their child for a longer period (over 9 months) than those who had more negative experiences. In line with recent research, breastfeeding behaviours were not associated with reported mother-infant bonding. CONCLUSIONS: Mothers who reported better birth experiences were most likely to breastfeed, and breastfeed for longer. We find no evidence to suggest that feeding methods are associated with bonding 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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".