The financing need for expanded maternity protection in Indonesia
Bibliographic record
Abstract
Background: Almost half of all Indonesian children under 6 months of age were not exclusive breastfed in 2017. Optimizing maternity protection programs may result in increased breastfeeding rates. This study aims to: estimate the potential cost implications of optimizing the current paid maternity protection program, estimate budgets needed to increase coverage of lactation rooms in mid and large firms, and explore challenges in its implementation in Indonesia. Methods: The potential cost implication of the current and increased maternity leave length (three and 6 months) as well as the potential budget impact to the government were estimated for 2020 to 2030. The cost of setting up lactation rooms in formal sector companies was estimated using the Alive & Thrive standards. Interviews were conducted in five different provinces to 29 respondents in 2016 to identify current and potential challenges in implementing both existing and improved maternity protection policies. Results: The costs of expanding paid maternity leave from three to 6 months and incorporating standardized lactation rooms in 80% of medium and large size firms in Indonesia was estimated at US$1.0 billion (US$616.4/mother per year) from 2020 to 2030, covering roughly 1.7 million females. The cost of setting up a basic lactation room in 80% of medium and large companies may reach US$18.1 million over 10 years. The three main barriers to increasing breastfeeding rates were: breastmilk substitutes marketing practices, the lack of lactation rooms in workplaces, and local customs that may hamper breastfeeding according to recommendations. Conclusions: The cost of expanding paid maternity leave is lower than the potential cost savings of US$ 1.5 billion from decreased child mortality and morbidity, maternal cancer rates and cognitive loss. Sharing the cost of paid maternity leave between government and the private sector may provide a feasible economic solution. The main barriers to increasing breastfeeding need to be overcome to reap the benefits of recommended breastfeeding practices.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".