Levels of Physical Activity, Patterns, and Perceived Barriers, Among University Students in Oman: A cross-Sectional Study
Bibliographic record
Abstract
Objective: To investigate the prevalence, pattern, and the perceived barriers, of physical activity among Omani university students studying in Oman.Methodology: A self-administered questionnaire using the short-form of the International Physical Activity Questionnaire (IPAQ) was disseminated to a selected sample of university students, from their second academic year onwards through Whats AppTM. Descriptive, Bivariate and multivariate analysis was conducted to measure patterns, levels and associated factors. Results: Overall 44% were classified as highly active, 30% as moderately active, and 26% as lowly active or inactive. Younger students (?22 years), male students, respondents with a positive perception of weight (normal or below), and self-perceived physically active (moderate to high) were more likely to engage in moderate to high physical activity. Students in university for ? 4 years (OR: 2.69) and students were members of sports youth clubs (OR: 2.76) were significantly more likely to engage in moderate or high physical activity. Lack of motivation was the top barrier of physical activity.Conclusions: More than a quarter of surveyed Omani university students were physically inactive which has the potential to have a detrimental effect on their health and well-being. Therefore, creating a conducive environment is essential for improving short and long-term health outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".