Level of physical activity among nurses and its associated factors: A cross-sectional study
Bibliographic record
Abstract
BackgroundPhysical activity is essential for preventing chronic disease and maintaining overall health. However, hospital nurses may face challenges maintaining adequate physical activity due to demanding work schedules and occupational stressors.ObjectiveTo examine the levels of physical activity among hospital nurses in Jordan and to identify demographic, occupational, and health-related factors associated with physical activity.MethodA cross-sectional survey was conducted among 750 nurses across Jordanian hospitals, with 597 respondents (80% response rate). Validated self-administered questionnaires were used to assess demographic, work characteristics, psychological well-being, sleep quality, musculoskeletal pain, and physical activity. Descriptive statistics summarized participant characteristics, and multiple linear regression was performed to identify independent associations with physical activity levels.ResultsThe mean age of participants was 32.1 years, and average work hours were 43.4 h per week. Approximately 31% of nurses report moderate physical activity levels, while 40% reported high physical activity levels. Higher physical activity levels were independently associated with longer work hours (β = 46.1; 95% CI: 1.9 to 90.2), more frequent night shifts (β = 163.8; 95% CI: 11.8 to 315.7), and more musculoskeletal pain sites (β = 254.9; 95% CI: 171.3 to 338.7). Having a chronic disease was significantly associated with lower physical activity (β = -1384.1; 95% CI: -2443.5 to -324.1).ConclusionMost nurses met recommended physical activity levels, and their engagement in physical activity was influenced by work demands and health status. Workplace health promotion initiatives should consider these factors to effectively support and sustain physical activity among hospital nurses.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".