A Wake-up Call for Burnout in Portuguese Physicians During the COVID-19 Outbreak: National Survey Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 outbreak has imposed physical and psychological pressure on health care professionals, including frontline physicians. Hence, evaluating the mental health status of physicians during the current pandemic is important to define future preventive guidelines among health care stakeholders. OBJECTIVE: In this study, we intended to study alterations in the mental health status of Portuguese physicians working at the frontline during the COVID-19 pandemic and potential sociodemographic factors influencing their mental health status. METHODS: A nationwide survey was conducted during May 4-25, 2020, to infer differences in mental health status (depression, anxiety, stress, and obsessive compulsive symptoms) between Portuguese physicians working at the frontline during the COVID-19 pandemic and other nonfrontline physicians. A representative sample of 420 participants stratified by age, sex, and the geographic region was analyzed (200 frontline and 220 nonfrontline participants). Moreover, we explored the influence of several sociodemographic factors on mental health variables including age, sex, living conditions, and household composition. RESULTS: Our results show that being female (β=1.1; t=2.5; P=.01) and working at the frontline (β=1.4; t=2.9; P=.004) are potential risk factors for stress. In contrast, having a house with green space was a potentially beneficial factor for stress (β=-1.5; t=-2.5; P=.01) and anxiety (β=-1.1; t=-2.4; P=.02). CONCLUSIONS: It is important to apply protective mental health measures for physicians to avoid the long-term effects of stress, such as burnout.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".