Physical inactivity, gender and culture in Arab countries: a systematic assessment of the literature
Bibliographic record
Abstract
BACKGROUND: Physical inactivity is associated with excess weight and adverse health outcomes. We synthesize the evidence on physical inactivity and its social determinants in Arab countries, with special attention to gender and cultural context. METHODS: We searched MEDLINE, Popline, and SSCI for articles published between 2000 and 2016, assessing the prevalence of physical inactivity and its social determinants. We also included national survey reports on physical activity, and searched for analyses of the social context of physical activity. RESULTS: We found 172 articles meeting inclusion criteria. Standardized data are available from surveys by the World Health Organization for almost all countries, but journal articles show great variability in definitions, measurements and methodology. Prevalence of inactivity among adults and children/adolescents is high across countries, and is higher among women. Some determinants of physical inactivity in the region (age, gender, low education) are shared with other regions, but specific aspects of the cultural context of the region seem particularly discouraging of physical activity. We draw on social science studies to gain insights into why this is so. CONCLUSIONS: Physical inactivity among Arab adults and children/adolescents is high. Studies using harmonized approaches, rigorous analytic techniques and a deeper examination of context are needed to design appropriate interventions.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".