Resident physicians’ mental health during COVID-19: Advocating for supports during and post pandemic
Bibliographic record
Abstract
There is already considerable evidence of how this novel corona virus (COVID-19) has had a major impact on our mental health and wellbeing. We are reminded of the mental health consequences of previous infectious disease outbreaks, not only for the public, but for frontline healthcare workers. Yet the lived experiences of resident physicians are missing from this discussion despite them being essential to the COVID-19 response and continuing to provide care during this time. The author asserts that considering what is known about the mental health effects of frontline healthcare work during previous outbreaks, residents are at risk given their role as physicians. In addition to baseline systemic stressors that put residents at risk of mental distress, they also face COVID-19 related stressors that exacerbate the risk given their role as trainees too. The author acknowledges and welcomes several rapid responses to residents' developing mental health needs from medical leaders across Canadian hospitals, programs, and resident bodies. Ultimately, however, medical leaders need to advocate for and implement changes that will support residents' mental health now and in the long-term well after COVID-19 has left its mark.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".