Impact of SARS, H1N1, and COVID-19 on Medical Trainees’ Academic and Personal Experience: A Systematic Search and Narrative Review
Bibliographic record
Abstract
OBJECTIVES: The SARS-CoV-2 pandemic is a destabilizing experience for medical students and resident doctors and troubles their training in the hospital setting. This narrative review aims to identify the effect of health crises on the academic and personal lives of medical trainees and to develop solutions to support them. METHODS: EducationSource, MedLine and PsychInfo were consulted on June 30th and December 16th, 2020 to identify the articles explaining the effect of SARS-CoV-1 (2002), A/H1N1 (2009) or SARS-CoV-2 (ongoing) on medical learners. Exclusion criteria included policy papers, letters to the editor or articles detailing the impact on undergraduate medical curricula, on nonmedical trainees, on the residency application process, or the physical impact of the disease. The quality of the selected papers was appraised using CASP for qualitative studies and NHLBI-NIH for cross-sectional studies. RESULTS: Ninety-four manuscripts were initially generated and 229, secondarily, of which respectively 14 and 16 were included in the final analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and reviewed qualitatively. It was found that the learners consider their education compromised by exam delays, the suspension of academic activities, and elective surgeries. Anxiety associated with this academic disruption developed. Burnout is exacerbated by the heightened workload. The main difference between the two searches was the long-term effect of COVID-19, including the opportunity for didactic innovation, the worry regarding professional identity formation and the development of mental health issues. The proposed solutions varied from continuous access to mental health resources to the follow-up of learners' well-being. CONCLUSION: It would be interesting to assess the impact of medical trainees' specialty and country's development on their experience with COVID-19.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".