Translation and Psychometric Evaluation of the Arabic Version of the Breastfeeding Self-Efficacy Scale-Short Form Among Women in the United Arab Emirates
Bibliographic record
Abstract
BACKGROUND: Breastfeeding self-efficacy as a construct has been theoretically and empirically linked to exclusive breastfeeding in studies globally using the Breastfeeding Self-Efficacy Scale-Short Form (BSES-SF). However, its application in the Middle East and North Africa region is limited, as it has not been validated. RESEARCH AIMS: To psychometrically validate the BSES-SF among a sample of mothers in the United Arab Emirates. METHODS: = 457) residing in the United Arab Emirates. We used translation techniques, item-test and item-total correlations, confirmatory factor analysis, tests of reliability, and tests of validity. RESULTS: Item-test correlations of scale items ranged from 0.67 to 0.84, while item-total correlations ranged from 0.58 to 0.81. The confirmatory factor model assessed the 14-item scale to be unidimensional with satisfactory model fit indices. Our findings suggested the Arabic-language version of the BSES-SF was a reliable measure (α = 0.95) with strong construct and discriminant validity. BSES-SF scores significantly predicted exclusive breastfeeding (aOR = 1.04; 95% CI [1.02, 1.08]) and exclusive duration (β = .06; 95% CI [0.4, 0.08]), which suggested strong predictive, validity after adjusting for parity, maternal age, maternal education, and study site. CONCLUSIONS: We have provided rigorous evidence that the BSES-SF is a valid and reliable measure of breastfeeding self-efficacy among Arabic-speaking women in the UAE. Interventions designed specifically to increase breastfeeding self-efficacy among Arabic-speaking women may be a mechanism to increase the suboptimal rates of breastfeeding exclusivity occurring in much of the MENA region.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".