Repercussions of the COVID-19 pandemic on the well-being and training of medical clerks: a pan-Canadian survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has been an unprecedented and potentially stressful event that inserted itself into the 2019-2020 Canadian medical curriculum. However, its impact on stress and subsequent professional pathways is not well understood. This study aims to assess the impact of the COVID-19 pandemic on the mental well-being, training, and career choices of Canadian medical clerks within the first three months of the pandemic. It also aims to assess their use of university support systems and their appreciation of potential solutions to common academic stressors. METHODS: An electronic survey composed of four sections: demographics, stressors experienced during the pandemic, World Health Organization (WHO) well-being index, and stress management and resources was distributed to Canadian clerks. RESULTS: Clerks from 10 of the 17 Canadian medical faculties participated in this study (n = 627). Forty-five percent of clerks reported higher levels of stress than usual; 22% reconsidered their residency choice; and 19% reconsidered medicine as a career. The factors that were most stressful among clerks were: the means of return to rotations; decreased opportunities to be productive in view of residency match; and taking the national licensing exam after the beginning of residency. The mean WHO well-being index was 14.8/25 ± 4.5, indicating a poor level of well-being among a considerable proportion of students. Clerks who reconsidered their residency choice or medicine as a career had lower mean WHO well-being indices. Most clerks agreed with the following suggested solutions: training sessions on the clinical management of COVID-19 cases; being allowed to submit fewer reference letters when applying to residency; and having protected time to study for their licensing exam during residency. Overall, clerks were less concerned with being infected during their rotations than with the impact of the pandemic on their future career and residency match. CONCLUSION: The COVID-19 pandemic had a considerable impact on the medical curriculum and well-being of clerks. A number of student-identified solutions were proposed to reduce stress. The implementation of these solutions throughout the Canadian medical training system should be considered.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".