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Record W4288690024 · doi:10.1177/10547738221112756

Psychometric Assessment of the Breastfeeding Self-Efficacy Scale-Short Form: A Confirmatory Factor Analysis of Indonesian Mothers

2022· article· en· W4288690024 on OpenAlexaff
Ayyu Sandhi, Cindy‐Lee Dennis, Shu‐Yu Kuo

Bibliographic record

VenueClinical Nursing Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversity of Toronto
FundersMinistry of Science and Technology, Taiwan
KeywordsConfirmatory factor analysisCronbach's alphaBreastfeedingHospital Anxiety and Depression ScaleClinical psychologyPsychologyConstruct validityAnxietyEdinburgh Postnatal Depression ScaleScale (ratio)PsychometricsPsychiatryMedicineStructural equation modelingDepressive symptomsPediatrics

Abstract

fetched live from OpenAlex

This study aimed to evaluate the psychometric properties of the Breastfeeding Self-Efficacy Scale-Short Form (BSES-SF) in Indonesian postpartum women. We conducted a cross-sectional study including 237 postpartum women in Yogyakarta City, Indonesia. Participants completed the BSES-SF, Edinburgh Postnatal Depression Scale (EPDS), and Hospital Anxiety and Depression Scale (HADS). Construct validity, internal reliability, and divergent validity were examined using confirmatory factor analysis, Cronbach’s alpha, and Pearson’s correlations. We identified a unidimensional structure through confirmatory factor analysis with an excellent internal consistency by Cronbach’s alpha of .90. Divergent validity was evidenced by low correlation of the BSES-SF with the depressive symptoms (EPDS, r = −.21; HADS-D, r = −.17, p < .05) and anxiety symptoms (HADS-A, r = −.15, p = .02). It is concluded that the Indonesian version of BSES-SF is a valid and reliable measurement tool to assess breastfeeding self-efficacy among postpartum women.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.148
GPT teacher head0.514
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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