Investigating Predictors of Prenatal Breastfeeding Self-Efficacy
Bibliographic record
Abstract
Background While breastfeeding is considered the optimal method of infant feeding, Canadian breastfeeding rates remain below the World Health Organization recommendations. Breastfeeding self-efficacy is known to positively influence breastfeeding outcomes. While previous research has identified predictors of breastfeeding self-efficacy in the immediate postpartum, this study identified predictors of breastfeeding self-efficacy in the prenatal period. Research aim: The aim of this study was to identify predictors of breastfeeding self-efficacy in the prenatal period among both primiparous and multiparous women. Methods A sample of 401 Canadian pregnant women in their third trimester completed an online survey. Stepwise multiple linear regression identified predictors of breastfeeding self-efficacy. Results The following variables explained 41.2% of the variance in breastfeeding self-efficacy among the entire sample: feeling prepared for labor and birth, number of children, breastfeeding knowledge, anxiety, length of plan to exclusively breastfeed, income, plan to exclusively breastfeed, and type of health-care provider. Among primiparous women, the following variables explained 31.6% of the variance in breastfeeding self-efficacy: feeling prepared for labor and birth, income, anxiety, length of plan to exclusively breastfeed, education, and marital status. Among the multiparous women, the following variables explained 33.6% of the variance in breastfeeding self-efficacy: anxiety, length of prior exclusive breastfeeding experience, breastfeeding knowledge, and plan to exclusively breastfeed. Conclusion Through the identification of predictors of breastfeeding self-efficacy in the prenatal period, health-care providers can strategically target women at risk of low breastfeeding self-efficacy and intervene early to promote breastfeeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".