Age-Related Mental Health Consequences of COVID-19: A Global Perspective
Bibliographic record
Abstract
Purpose: The Société Internationale d’Urologie (SIU) conducted a survey to determine whether the pandemic has harmed the mental health of practicing urologists worldwide. Methods: Members of the Executive Board of the SIU designed a self-selected survey consisting of multiple-choice questions about the safety and mental health of urologists during the COVID-19 pandemic. The survey was disseminated by email to SIU members worldwide. Results: A total of 3448 SIU members from 109 countries responded to the survey, which sought to determine the extent of mental health symptoms, including depression, anxiety, insomnia, and distress—experienced during the COVID-19 pandemic. Overall, 21% of urologists who responded reported that their mental health was very challenged, with 58% indicating increased stress levels, and 15% indicating greatly increased stress levels. Older urologists were less likely to report any of the negative mental health symptom queried (ie, delirium [rs = −0.06, P = 0.001], psychosis [rs = −0.04, P = 0.019], anxiety [rs = −0.09, P < 0.001], depression [rs = −0.08, P <.001], distress [rs = −0.07, P < 0.001]), except insomnia (P > 0.20). Furthermore, 29% of urologists indicated they were afraid to go to work, while 53% reported being afraid to go home to their families after work. Conclusion: In this worldwide survey of practicing urologists, more than half of the participants reported an increase in insomnia, distress, and other psychological symptoms as they managed patients during the COVID-19 pandemic, although half of respondents did not experience any mental health symptoms. Institutions should provide psychological coping resources to all health care staff, not only for the front-line workers during the pandemic.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".