Rural communities are more physically active in Indonesia: the results on Indonesian national survey data
Bibliographic record
Abstract
Introduction: Physical activity is an important component of maintaining physical and mental health, including reducing the risk of chronic diseases and improving cognitive function. However, nearly a quarter of the global adult population is physically inactive, with factors such as geographic location (urban vs. rural) and socioeconomic conditions influencing participation levels. Objective: To identify differences in physical activity levels between urban and rural communities in Indonesia and to identify the influence of residential location, wealth, and age on physical activity. Methodology: This study used data from the 5th wave of the Indonesian Family Life Survey (IFLS), with a sample of 20,611 respondents (57.4% urban, 42.6% rural). Physical activity levels were measured based on the Metabolic Equivalent of Task (MET) and analyzed using ANCOVA (with residence as a fixed factor, and wealth and age as covariates) and Bayesian ANCOVA to compare predictive models. Results: Significant difference in physical activity between urban and rural areas (F = 100.893, *p* < 0.001), with rural communities being more active. Wealth level had a significant effect (F = 44.894, *p* < 0.001), while age did not (*p* = 0.428). The best model in the Bayesian analysis included both residence and wealth (posterior probability: 96.7%), confirming the importance of geographic and economic context. Conclusions: Rural communities in Indonesia are more physically active, compared to urban communities. Public health policies need to prioritize location-based interventions. Further studies are needed to explore other factors such as access to infrastructure and community perceptions of physical activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".