Predictors of the Use of Traditional Medicines in the Universal Health Coverage System in Indonesia
Bibliographic record
Abstract
Background: Indonesia has committed to achieving Universal Health Coverage (UHC) and introduced national health insurance (JKN) to meet that commitment. Despite the increasing availability of healthcare services under the JKN scheme, traditional medicine (TM) continues to be a significant part of healthcare for Indonesians. In the context of the UHC system, this study aims to examine the predictors of TM use among urban and rural communities in Indonesia. Methods: A cross-sectional survey was conducted using a semi-structured questionnaire targeting urban and rural communities. A total of 926 households were randomly selected to participate in the survey. Multivariate logistic regression analysis was used to identify the significant predictors of TM use. Results: Multivariate analysis revealed the following variables to be predictive of TM use, namely, rural residence, being more educated, experiencing some health problem, demonstrating ethnomedical knowledge, having a favourable opinion about the safety and efficacy of TM and holistic orientation to health. On the other hand, working in the formal sector was associated with lower odds of using TM compared to those who were employed in the informal sector. Conclusion: People’s experience, personal attributes, and attitude towards TM, rather than dissatisfaction with healthcare service, predicted the likelihood of using TM in the UHC system in Indonesia. This finding also implies the underutilisation of JKN services by the insured TM users living in rural areas. Considering the community’s strong preferences for TM, this paper argues that its inclusion in the JKN system may increase the utilisation of the JKN service.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".