The Effect of Ramadan and COVID-19 on the Relationship between Physical Activity and Burnout among Teachers
Bibliographic record
Abstract
The objective of this study was to explore the effect of COVID-19 and Ramadan on physical activity (PA) and burnout in teachers and the relationship between them. A total of 57 secondary school teachers from public education centers participated in the present study. They were aged between 29 and 52 years. To determine the effect of Ramadan and COVID-19 on PA and burnout, participants completed the online questionnaires before COVID-19, one week before Ramadan and during the second week of Ramadan. The International Physical Activity Questionnaire-BREF and the Maslach Burnout Inventory-Human Services Survey were used to assess PA intensities and burnout, respectively. The data revealed that total PA (p < 0.001, p < 0.05, respectively) vigorous metabolic equivalent of task (MET) (p < 0.001, p < 0.05, respectively), moderate MET (p < 0.001, p < 0.01, respectively) were higher before COVID-19 and before Ramadan than during Ramadan. Regarding burnout subscales, emotional exhaustion (p < 0.001, p < 0.01, respectively) was higher before Ramadan than before COVID-19 and during Ramadan. A lower personal accomplishment was reported before Ramadan than before COVID-19 and during Ramadan (both p < 0.05). In addition, low to high correlations were observed between PA intensities and burnout subscales, except for the correlation between depersonalization and all PA intensities. In conclusion, Ramadan intermittent fasting along with PA was highly recommended for teachers and the general population to improve positive emotions and general health.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".