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 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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".