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Record W2992620654 · doi:10.3390/nu11122988

Determinants of Early Initiation of Breastfeeding among Mothers of Children Aged Less Than 24 Months in Northwestern Romania

2019· article· en· W2992620654 on OpenAlexfundno aff
Anamaria Cozma-Petruț, Ioana Badiu-Tişa, Oana Stanciu, Lorena Filip, Roxana Banc, Laura Ioana Gavrilaș, Daniela Ciobârcă, Simona Codruţa Hegheş, Doina Miere

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsBreastfeedingMedicineOdds ratioConfidence intervalLogistic regressionDemographyBreast feedingCross-sectional studyMultivariate analysisOddsObstetricsPediatrics

Abstract

fetched live from OpenAlex

Early initiation of breastfeeding (EIBF), defined as putting newborns to the breast within 1 h of birth, may have important benefits for both infant and mother. The aim of this study was to assess EIBF practices and its determinants in northwestern Romania. This cross-sectional study was conducted from March to June 2019, based on a sample of 1399 mothers of children aged less than 24 months. The sample was recruited from the community, from 29 cities and 41 communes distributed across the six counties of the northwestern region of Romania. Mothers responded by face-to-face interviews to a structured questionnaire. Multivariate logistic regression was used to identify factors independently associated with EIBF. Only 24.3% of the mothers initiated breastfeeding within 1 h of birth. Delivering at a private hospital (adjusted odds ratio (AOR): 5.17, 95% confidence interval (CI) 3.87, 6.91), vaginal delivery (AOR: 4.39, 95% CI 3.29, 5.88), mother-newborn skin-to-skin contact for 1 h or more (AOR: 55.6, 95% CI 23.0, 134.2), and breastfeeding counseling during antenatal visits (AOR: 1.48, 95% CI 1.12, 1.97) were factors associated with increased likelihood of EIBF. Overall, the practice of EIBF was poor. Targeting modifiable factors associated with EIBF may be used to improve early initiation practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.838

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.264
Teacher spread0.247 · 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 teacher head, 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

Citations28
Published2019
Admission routes1
Has abstractyes

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