Revitalizing Rural Healthcare: A Case Study of Village Maternity Cottages in Indonesia
Bibliographic record
Abstract
Disparities in health facilities between urban and rural areas significantly hamper health services in rural communities.Due to the high maternal and newborn mortality rates in East Nusa Tenggara Province, the Indonesian government revitalized health services through the Village Maternity Cottages Program.This study seeks to describe the index of community satisfaction with the quality of service at the Village Maternity Cottages.In addition, it provides strategic planning for the program in realizing long-term sustainable development of health services.This research uses a Mix Method approach.Data collection methods included observation, interview, and questionnaire distribution.Primary data were collected from 78 participants who accessed the Village Maternity Cottages health services in Suanae Village, East Nusa Tenggara Province.The results showed that the Village Maternity Cottages User Community Satisfaction Index obtained an average rating of good, to very good in fourteen categories of health services.Strategic planning can be done by the Regional Head must develop a Village Maternity Cottages Policy that sets out steps to achieve sustainable health service development goals.The government's ability to empower groups, build partnerships, develop strategic planning, funding stability, program evaluation, and environmental support as sustainable program development in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".