Identifying predictors of breastfeeding self‐efficacy in the immediate postpartum period
Bibliographic record
Abstract
Researchers have found evidence that breastfeeding self-efficacy is an important variable that significantly influences initiation and duration rates. The purpose of this study was to develop a multi-factorial predictive model of breastfeeding self-efficacy in the first week postpartum. As part of a longitudinal study, a population-based sample of 522 breastfeeding mothers in a health region near Vancouver, British Columbia completed mailed questionnaires at 1-week postpartum. Bivariate correlations were used to select variables for the multiple regression analysis. The best-fit regression model revealed eight variables that explained 54% of the variance in Breastfeeding Self Efficacy Scale (BSES) scores at 1-week postpartum: maternal education, support from other women with children, type of delivery, satisfaction with labor pain relief, satisfaction with postpartum care, perceptions of breastfeeding progress, infant feeding method as planned, and maternal anxiety. The BSES may be used to identify risk factors, enabling health professionals to improve quality of care for new breastfeeding mothers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".