Psychometric testing of the breastfeeding self‐efficacy scale‐short form in a sample of Black women in the United States
Bibliographic record
Abstract
The benefits of breastfeeding increase with duration and exclusivity, but significant racial disparities exist in breastfeeding rates. Breastfeeding self-efficacy, as measured by the Breastfeeding Self-Efficacy Scale Short-Form (BSES-SF), is a significant predictor of breastfeeding outcomes in diverse samples. The purpose of this study was to assess the psychometric properties of the BSES-SF in Black women in the US. The psychometric characteristics were consistent with previous studies, including internal consistency, comparison with contrasted groups, and correlation with the construct of breastfeeding network support. Breastfeeding self-efficacy significantly predicted breastfeeding at 4 and 24 weeks postpartum. The results are consistent with previous research, and they suggest the BSES-SF could be used to identify women at risk for prematurely discontinuing 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".