Psychometric Assessment of the Brazilian Version of the Breastfeeding Self‐Efficacy Scale
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to translate and psychometrically assess the Breastfeeding Self-Efficacy Scale (BSES) among women living in Fortaleza, CE, Brazil, and examine the relationship between breastfeeding self-efficacy (BSE) and maternal demographic variables. DESIGN AND SAMPLE: This methodological study is the first translation of BSES conducted in South America. The psychometric assessment of the original study was replicated. This methodological study enrolled a group (sample of judgment) of 117 pregnant women. MEASURES: BSES and maternal sociodemographic variables were studied. RESULTS: The Cronbach's alpha coefficient for the translated BSES was .88. Significant differences in BSES scores were found among mothers with a previous satisfactory breastfeeding experience (M = 145.81; SD = + or - 6.82, p = .0001). Significant relationships were found among prenatal BSE and maternal age (r = .228; p = .01), educational level (r = .234; p = .01), and marital status (r = .183; p = .04). No relationship was found among BSE and maternal occupation, family income, or number of pregnancies. CONCLUSIONS: Altogether, our findings suggest that BSES translated into Portuguese may be a reliable and valid measure to assess maternal BSE in Brazilian culture. Minor changes may be needed to use it in other Portuguese-speaking countries, such as Portugal and Mozambique.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".