Mental Health and Professional Burnout among Residents During the Covid-19 Pandemic: Situational and Psychological Factors
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Bibliographic record
Abstract
The paper presents the results of a study of the level and factors of mental malad- justment and professional burnout of medical residents undergoing training at the Training Center of N.V. Sklifosovsky Research Institute for Emergency Medicine during the second wave of the COVID-19 pandemic. The study involved 110 first and second year residents (30 men and 80 women; mean age — 25.1±2.32), both working in the COVID-19 “red zone” and helping other patients. The follow- ing methods were used to assess symptoms and factors of mental maladjustment and professional burnout: Beck Depression and Anxiety Scales (Beck et al., 1988; 1996), Maslach Burnout Inventory (Maslach & Jackson, 1981), PTSD Checklist for DSM 5 (PSL-5; Weathers et al., 2013) Distress Thermometer (Holland, Bultz, 2007), UCLA Loneliness Scale (Russell et al., 1978) Three-Factor Perfectionism Inventory (Garanyan et al., 2018) and Toronto Alexithymia Scale (Taylor et al., 2003). According to the data, 43% of young doctors noted symptoms of depression of moderate and high severity, suicidal thoughts were present in 10%, symptoms of heightened anxiety in 30%, and more than a half (55%) had critically high rates of symptoms of post-traumatic stress. About a quarter of the respondents showed high rates of general distress (24%) and professional burnout in all three of its as- pects (emotional exhaustion — 21%, depersonalization — 23%, and personal ac- complishment — 22%). Most residents associated distress with difficulties in com- bining work and study and fear for the quality of education during the pandemic. Social support was noted as a factor in coping with stress. A series of regression analyzes showed the importance of the contribution of the experience of loneli- ness, as well as high rates of perfectionism and alexithymia, to mental distress and professional burnout of residents.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it