Psychometric Assessment of the Croatian Version of the Breastfeeding Self-Efficacy Scale–Short Form
Bibliographic record
Abstract
BACKGROUND: Many mothers find it difficult to breastfeed exclusively for the recommended 6 months postpartum. The Breastfeeding Self-Efficacy Scale-Short Form (BSES-SF) was developed to measure breastfeeding self-efficacy, an important predictor of breastfeeding outcomes. OBJECTIVE: To translate and psychometrically assess the BSES-SF among women in Croatia. METHODS: A convenience sample of 190 breastfeeding mothers was recruited from a Baby-Friendly hospital in Zagreb, Croatia. In-hospital mothers completed questionnaires that included the translated BSES-SF, Sense of Coherence Scale (SOC-13), and a demographic questionnaire. The follow-up questionnaires were administered to mothers at 1 and 6 months postpartum to determine their infant feeding method. RESULTS: The mean total score of the Croatian version of the BSES-SF was 55 ± 7. The Cronbach α coefficient for internal consistency was 0.86, suggesting good reliability. In-hospital BSES-SF scores significantly predicted breastfeeding duration and exclusivity at 1 and 6 months postpartum, providing support for predictive validity. The BSES-SF scores were significantly correlated with the total SOC scores (r = 0.32, P < .001) and the SOC subscales of comprehensibility (r = 0.35, P < .001), manageability (r = 0.26, P < .001), and meaningfulness (r = 0.20, P = .005), providing support for construct validity. CONCLUSION: This study provides evidence that the translated version of the BSES-SF may be a valid and reliable measure of breastfeeding self-efficacy among postpartum women in Croatia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".