The Mental Health Toll: Medical Trainees Living with Disabilities during the COVID‐19 Pandemic
Bibliographic record
Abstract
Objectives. Throughout the world, medical trainees have experienced psychological distress during the COVID‐19 pandemic due to substantial changes in their educational programs and COVID‐19 patient care. When medical trainees live with a disability, their psychological distress may be exacerbated. This study aimed to explore how having a disability may be associated with an additional emotional toll for medical trainees during the COVID‐19 pandemic. Methods. Participants in the study were 201 medical trainees (62 interns and 138 residents) and 147 medical attending physicians in various fields of medicine. Participants completed an online survey including the Fear of COVID‐19 scale, the Depression Anxiety and Stress Scale, and the Mental Health Continuum Scale measuring aspects of wellbeing, including emotional wellbeing, belonging, and sense of psychological meaning. Results. Disabled participants experienced higher levels of fear of COVID‐19, depression, anxiety, stress, and lower levels of emotional wellbeing, sense of belonging, and psychological meaning compared to participants with no disabilities. Residents generally experienced higher stress levels and lower wellbeing levels than attendings. Residents living with disabilities were more prone to experience stress and fear of COVID‐19, and residents and interns living with disabilities experienced higher levels of depression, anxiety, and stress compared to their peers who live without disabilities and compared to attendings living with disabilities. Conclusions. Medical trainees living with disabilities were more prone to experience high levels of psychological distress and lower levels of wellbeing during the COVID‐19 pandemic compared to their peers without disabilities. Therefore, there is a need to address this emotional toll and provide support in medical education programs promoting trainees’ wellbeing, sense of belonging, and psychological meaning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".