Women’s Membership in Health Insurance and Correlation with Contraception Use in Indonesia
Bibliographic record
Abstract
BACKGROUND & PURPOSE: One important effort in reducing the Maternal Mortality Rate is integration of Family Planning services into Health Insurance policy. This is giving affordability in health service financing through providing contraceptive accordance with established policy. The purpose of this study is to examine women’s participation in health insurance and correlations to contraception use. MATERIAL & METHODS: The study used the 2012 Indonesian Demographic and Health Survey data-set. Samples were women aged 15 to 49 years, of married status or living together (n=33,465). The dependent variable was contraception used for three categories: Using Long Term Contraceptive, using non-Long Term Contraceptive, and not using any kind of contraception. Data analysis used Chi-square and multinomial logistic regression with complex sample. RESULTS: 10.6% of women were found to have used a Long Term Contraception method. Health insurance membership has correlations to contraceptive use (OR=1.241 and 0.964, p<0.05, CI 95%), with confounder variables of age (p<0.05, OR=1.428 and 0.648), education (p<0.05, OR=1.402 and 1.064), work status (p<0.05, OR=1.151 and 0.966), parity (p<0.05, OR=3.114 and 1.685), perception of ideal number of children (p<0.05, OR=2.057 and 1.682), husband’s education (p<0.05, OR=0.166 and 0.920), husband's work (p<0.05, OR=1.247 and 2.469), and role of media (p<0.05, OR=1.255 and 1.084). CONCLUSION & RECOMMENDATIONS: This study was empirical evidence in Indonesia that health insurance factors have a significant correlation to Long Term Contraceptive use in women. It is recommended for government to maintain and improve policies that integrate Family Planning services into National Health Insurance.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".