The Breastfeeding Self-Efficacy Scale–Short Form (BSES-SF): German Translation and Psychometric Assessment
Bibliographic record
Abstract
BACKGROUND: German-speaking mothers have breastfeeding rates below the international breastfeeding recommendations. Previous research has found that breastfeeding self-efficacy is an important and modifiable predictor of breastfeeding outcomes, thus improving breastfeeding rates. The Breastfeeding Self-Efficacy Scale-Short Form (BSES-SF) is used in many countries to assess maternal breastfeeding self-efficacy. This instrument has not been available in German. RESEARCH AIMS: To translate the BSES-SF into German and assess its psychometric properties among breastfeeding mothers up to 12 weeks postpartum. METHODS: This cross-sectional study was conducted online with 355 breastfeeding mothers recruited from breastfeeding groups through Facebook. The BSES-SF was translated into German using forward and back-translation. To test reliability, item-total characteristics, including Cronbach's alpha, were examined. We used principal component analysis, as well as known-groups comparisons for evaluating construct validity, and examined the relationship between breastfeeding self-efficacy and demographic variables. RESULTS: = 4.32). The Cronbach's alpha coefficient was .88 and corrected item-total correlations ranged between .37 and .73. Principal components analysis yielded one component with factor loadings >.40 and an eigenvalue of 5.62, which explained 40% of the total variance. In addition, known group comparisons provided further evidence for construct validity. There was no significant difference in BSES-SF scores in terms of demographic and obstetrics characteristics. CONCLUSION: Our results provide evidence that the German version of the BSES-SF is a reliable and valid tool for measuring breastfeeding self-efficacy among mothers in German-speaking countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".