Psychometric characteristics of the breastfeeding self‐efficacy scale: Data from an Australian sample
Bibliographic record
Abstract
Many new mothers discontinue breastfeeding prematurely because of difficulties encountered rather than maternal choice. Research has shown that a significant predictor of breastfeeding duration is a mother's confidence in her ability to breastfeed. To measure breastfeeding confidence, the Breastfeeding Self-Efficacy Scale (BSES) was developed and psychometrically tested at 1 week postpartum. The purpose of this methodological study was to psychometrically test the BSES antenatally and at 1 week and 4 months postpartum in a sample of Australian women and to determine predictive validity. The psychometric assessment of the original BSES study was replicated, including internal consistency, principal components factor analysis, comparison of contrasted groups, and correlations with a similar construct. Support for predictive validity was demonstrated through positive correlations and significant mean differences between antenatal BSES scores and infant-feeding method at 1 week and 4 months postpartum. The BSES is now considered ready for both research and clinical use (a) to identify new mothers with low breastfeeding confidence who require additional assistance, (b) to assess breastfeeding behaviors and cognitions in order to have individualized confidence-building strategies, and (c) to assist in the evaluation of various nursing interventions.
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".