Socioeconomic gradient in physical activity: findings from the PERSIAN cohort study
Bibliographic record
Abstract
BACKGROUND: The level of socioeconomic-related inequality in physical activity in Iran is largely unknown. This study investigates socioeconomic-related inequality in poor-physical activity (PPA) among Iranian adults. METHODS: A total of 129,257 adult participants enrolled in the PERSIAN (Prospective Epidemiological Research Studies in IrAN) Cohort were included in this study. Physical activity of adults was measured using metabolic equivalent rates (METs). Physical activity less than 41 METs/hour/day was considered PPA. The Concentration index (C) was used to quantify socioeconomic-related inequality in PPA. Moreover, the C was decomposed to identify the relative contribution of explanatory variables to inequality in PPA. RESULTS: There were significant regional variations in physical activity level among Iranian adults (29.8-76.5%). The positive value of C (0.098, 95% CI = 0.092 to 0.104) suggested that the higher concentration of PPA among higher socioeconomic status (SES) adults in Iran which was consistently observed in all cohort sites. CONCLUSIONS: The higher prevalence of PPA among Iranian adults, especially, women and older adults, warrant further public health attention. Since PPA is concentrated more among the high-SES population in Iran, strategies for the promotion of physical activity should focus more on economically well-off population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".