The relationship between physical activity and burnout among respiratory therapists in Jeddah City, Saudi Arabia
Bibliographic record
Abstract
Background/objective: Burnout is a condition in which a person feels physically fatigued and mentally drained. It occurs after a long period of work-related stress and may lead to mental disorders, such as depression and anxiety. Therefore, healthcare providers especially require early intervention. Regular physical activity has been reported to benefit individuals with mental illness, suggesting that a relationship between physical activity and burnout might exist. Hence, this study's objective was to analyze the relationship between physical activity and burnout among respiratory therapists and student interns in Jeddah City, Saudi Arabia. Methods: A cross-sectional descriptive study with respiratory therapists and interns working in public and private hospitals was conducted from November 2, 2020, to November 27, 2020. Participants responded to an electronic survey consisting of the Maslach Burnout Inventory - Human Services Survey for Medical Personnel, which measures the burnout dimensions of emotional exhaustion, depersonalization, and personal accomplishment. They also completed the International Physical Activity Questionnaire - Long Form and a demographic questionnaire. Results: Among the 250 eligible respiratory therapists and interns, data from the 152 respondents who completed the electronic survey were analyzed. Although no association between physical activity and burnout was found, a significant effect of the novel coronavirus disease 2019 (COVID-19) on physical activity and significant associations of high burnout (emotional exhaustion) with nationality and smoking were found. Conclusion: No association was found between burnout level and physical activity. Confounding factors, such as the COVID-19 pandemic during the study's data collection and analyses, likely contributed to the study's findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".