Doctors’ Professional and Personal Reflections: A Qualitative Exploration of Physicians’ Views and Coping during the COVID-19 Pandemic
Bibliographic record
Abstract
Numerous studies have examined the risks for anxiety and depression experienced by physicians during the COVID-19 pandemic. Still, qualitative studies investigating physicians' views, and their discovered strengths, are lacking. Our research fills this gap by exploring professional and personal reflections developed by physicians from various specialties during the pandemic. Semi-structured interviews were conducted with physicians practicing in the province of Saskatchewan, Canada, during November 2020-July 2021. Thematic analysis identified core themes and subthemes. Seventeen physicians, including nine males and eight females, from eleven specialties completed the interviews. The pandemic brought to the forefront life's temporality and a new appreciation for life, work, and each other. Most physicians found strength in values, such as gratitude, solidarity, and faith in human potential, to anchor them professionally and personally. A new need for personal fulfilment and hybrid care emerged. Negative feelings of anger, fear, uncertainty, and frustration were due to overwhelming pressures, while feelings of injustice and betrayal were caused by human or system failures. The physicians' appreciation for life and family and their faith in humanity and science were the primary coping strategies used to build adaptation and overcome negative emotions. These reflections are summarized, and implications for prevention and resilience are discussed.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".