Psychometric Assessment of the Breastfeeding Self-Efficacy Scale-Short Form: A Confirmatory Factor Analysis in Malawian Mothers
Bibliographic record
Abstract
Background: Exclusive breastfeeding to 6 months postpartum has been related to breastfeeding self-efficacy in diverse populations. Globally, this is measured using the Breastfeeding Self-Efficacy Scale–Short Form (BSES-SF). Research Aim: To evaluate the psychometric properties of the BSES-SF among women in Malawi; and to examine the relationship between breastfeeding self-efficacy and demographic and health factors. Methods: The study design was a prospective, cross-sectional survey with a 2 week follow-up reliability check. Postpartum women ( N = 180) were recruited at a maternity hospital in Lilongwe, Malawi. In addition to the BSES-SF, the World Health Organization's Quality of Life Scale (QoL) was also administered. Furthermore, confirmatory factor analysis, Cronbach's alpha, and Pearson's correlations were used to examine the construct validity, reliability, test-retest reliability, and convergent validity. Results: The confirmatory factor analysis supported a unidimensional structure of the Malawian version of the 12-item BSES-SF. Cronbach's alpha and the intra-class correlation coefficient were 0.79 and 0.75, respectively. BSES-SF scores had significant correlation with QoL domains (physical QoL: r = 0.31, p < .001; and environmental QoL: r = 0.22, p < .01). Participants’ age, parity, and mode of delivery were positively correlated with breastfeeding self-efficacy scores. Conclusion: The findings of our study confirmed that the 12-item BSES-SF is a reliable and valid scale for assessing women’s breastfeeding self-efficacy in Malawi.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".