Effect of Mediterranean diet and physical activity on healthcare professional depression, burnout and professional fulfillment during COVID-19
Bibliographic record
Abstract
Objectives. The mental health of healthcare professionals, especially during the COVID-19 pandemic, is a critical concern. This study investigates the prevalence of burnout and professional fulfillment, depression and the relationship between physical activity and adherence to the Mediterranean diet with depression, burnout and professional fulfillment among healthcare professionals. Methods. Data were collected through a web-based survey of 567 healthcare professionals. Logistic regression analysis with age and sex adjustment was employed to analyze the results. Results. The prevalence of depression was 44%, burnout stood at 66% and professional fulfillment was reported at 28%. Adherence to the Mediterranean diet was linked to a reduced risk of depression (odds ratio [OR] 0.63, 95% confidence interval [CI] [0.41, 0.96], p = 0.033) and physical activity was also associated with a lower risk of depression (OR 0.49, 95% CI [0.32, 0.75], p = 0.001). Furthermore, adherence to the Mediterranean diet was associated with a reduced risk of burnout (OR 0.49, 95% CI [0.25, 0.98], p = 0.045). Conclusion. This study highlights the positive impact of physical activity and the Mediterranean diet as lifestyle factors on depression and burnout. These findings have implications for screening, follow-up and timely interventions to support the mental well-being of healthcare professionals.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".