Physicians’ mental health and coping during the COVID-19 pandemic: One year exploration
Bibliographic record
Abstract
Numerous cross-sectional studies have examined physicians' health and coping during the COVID-19 pandemic, while longitudinal studies are lacking. This study explores the progression over one year of physicians' physical and mental health symptoms, their strategies used to cope and discusses coping strategies in relation to physical and mental health symptoms. Two surveys, one year apart, exploring physicians' physical, mental health symptoms and employed coping strategies were sent to all physicians practicing in the province of Saskatchewan, Canada. A total of 117 physicians participated in Round I (RI) (November 2020–January 2021) and 158 participated in Round II (RII) (October 2021–February 2022). Physicians' physical and mental health symptoms remained high, irrespective of their specialty or COVID-19 exposure. COVID-related Post-Traumatic Stress Disorder increased by five times at RII ( p = 0.02). In RI anxiety was most prevalent in middle-aged females. In RII depression was most prevalent in physicians with no children. Most coping was adaptive (90%) and included Behavioural, Relational, Cognitive, Spiritual, and Interventional strategies. After one-year, Spiritual coping decreased, while Interventional coping increased by eight times ( p = 0.01). Despite efforts to employ adaptive coping, physicians' rates of psychological and physical health difficulties remained high or worsened over one year, offering insight into the protracted health care crisis, and the need for solutions . Our observation of physicians' needs for additional supports, camaraderie and appreciation as well as the shift in coping strategies as the pandemic progressed, offer targets for interventions meant to promote recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".