Breastfeeding Practices and Associated Factors in Shanghai: A Cross-Sectional Study
Bibliographic record
Abstract
The status of breastfeeding practices remains unsatisfactory across China, but regional differences persist. However, disaggregated data for specific provinces are limited. This representative survey determined the status of breastfeeding and factors associated with breastfeeding practices in Shanghai. The questionnaire was designed in compliance with indicators for assessing infant and young child-feeding practices defined by the World Health Organization and the United Nations Children's Fund (UNICEF). A total of 2665 children aged two years and younger (0-730 days) were investigated, among whom 1677 were aged under six months. The early initiation of breastfeeding (EIBF) rate was 60.3%. Among children aged under six months, 43.4% were exclusively breastfed (EBF). The univariate regression analysis showed that the EBF rate was influenced by multiple factors, including individual, socioeconomic, workplace and employment, and health system. The subsequent multivariate analysis suggested that mothers with a higher rate of EBF shared the following characteristics: intention to breastfeed during pregnancy, breastfeeding knowledge, and higher satisfaction with support through the healthcare system after delivery. The rate of EBF in Shanghai is over 40%, and supporting breastfeeding requires measures at multiple levels, including individual attributes, women's work and employment conditions, breastfeeding knowledge, and health services.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".