Non Communicable Disease (NCD) as Risk for Disability: Recommendation for Indonesian UHC Program
Bibliographic record
Abstract
BACKGROUND: Disability is health condition drives people seek treatment. Information on magnitude of disability and its contributors is important in Indonesian universal health coverage (UHC) era. It is useful for cost estimation, as well as to design type of service needed at the time being and in the future. This research intends to assess magnitude of disability and its non-communicable diseases (NCD) as risk. Disability obtained from WHODAS 2 score. METHOD: data obtained from 2018 National Health Survey (Riskesdas 2018) sample age 18 – 59, consisted of 528762 respondents. Dependent variable is disability measured using WHODAS2. Independent variables are NCD consist of statements ever diagnosed asthma, cancer, diabetes, heart, stroke, chronic renal failure, and joint disease by healthcare personnel and emotional distress which is score obtained from self-report questionnaire (SRQ) instrument. STATISTIC ANALYSIS: Validation between disability and 2 diseases was performed using Chi Square analysis. Logistic regression analysis was applied to identify contribution of NCD on disability. RESULTS: Results show risk of NCD on disability in the working age group of 18–59 years. Stroke and emotional distress are the highest contributors with OR more than 3. Results can serve as input for UHC program to estimate costs of working age health service, including rehabilitation. The Ministry of Health can develop or improve current health system with comprehensive services provision including psychological intervention in rehabilitation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".