Translation and Validation of the Breastfeeding Self‐Efficacy Scale Into Chinese
Bibliographic record
Abstract
The purpose of this methodological study was to translate the Breastfeeding Self-Efficacy Scale (BSES) into Mandarin and determine the psychometric characteristics among a sample of 186 Chinese women. The BSES was translated and pilot-tested with 21 breastfeeding women. Following minor revisions to ensure content and semantic equivalence, the instrument was administered to 186 hospitalized breastfeeding women, who were then contacted again at 4 and 8 weeks postpartum to determine their infant feeding method and readminister the Edinburgh Postnatal Depression Scale. The psychometric assessment method used to validate the original BSES (English version) was replicated with the translated Chinese version. The Cronbach's alpha coefficient for the Chinese version BSES was 0.93, suggesting good reliability. Consistent with the English version, the factor analysis procedures resulted in the theorized two-factor solution and as hypothesized, the BSES was significantly correlated with the Edinburgh Postnatal Depression Scale, a theoretically related construct. Support for predictive validity was demonstrated through significant mean differences in BSES scores between breastfeeding and bottle-feeding mothers at 4 and 8 weeks postpartum. The results from this study are the first to examine the psychometric characteristics of the Chinese version BSES administered in a hospital postnatally and provide further evidence of the reliability and validity of the instrument.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".