Management Commitments in the Policy Implementation of Exclusive Breast-Feeding for Working Mothers: A Study in Textile Industries in Central Java
Bibliographic record
Abstract
The high rates of neonatal, infant and children mortality in Indonesia indicate that the MDGs 2017 target has not been fully achieved. One of the prevention measures for mortality is exclusive breastfeeding. In Indonesia, the breast-feeding coverage has not reached the expected target because one of the factors is working mothers. Most working mothers stop breastfeeding or start mixing baby feeding before the baby is 6 months old. Here, the commitment of company managers in implementing government policies will increase exclusive breast-feeding coverage. This type of research uses descriptive analysis with documentation study methods, observation, interviews and questionnaires in 4 textile companies in Semarang City, Semarang Regency and Pekalongan. The number of respondents 99 people consists of women workers, administrators of workplaces and health workers in the workplace. The results of the study show that 49.5% of companies are committed to supporting government policies by facilitating leave and that there are arrangements for working hours that are in accordance with the rules, and that 50.5% do not support this matter and do not fully support government policy. Furthermore, the results of this study are complemented by interviews with HRD and management related to breastfeeding policy and working conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".