Is online learning during the COVID-19 pandemic associated with increased burnout in medical learners?: A medical school’s experience
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic necessitated a shift to virtual curriculum delivery at Canadian medical schools. At the NOSM University, some learners transitioned to entirely online learning, while others continued in-person, in-clinic learning. This study aimed to show that medical learners who transitioned to exclusively online learning exhibited higher levels of burnout compared to their peers who continued in-person, clinical learning. Analysis of factors that protect against burnout including resilience, mindfulness, and self-compassion exhibited by online and in-person learners at NOSM University during this curriculum shift were also explored. METHODS: As part of a pilot wellness initiative, a cross-sectional online survey-based study of learner wellness was conducted at NOSM University during the 2020-2021 academic year. Seventy-four learners responded. The survey utilized the Maslach Burnout Inventory, the Brief Resilience Scale, Cognitive and Affective Mindfulness Scale-Revised, and the Self-Compassion Scale-Short Form. T-tests were utilized to compare these parameters in those who studied exclusively online and those who continued learning in-person in a clinical setting. RESULTS: Medical learners who engaged in online learning exhibited significantly higher levels of burnout when compared with learners who continued in-person learning in a clinical setting, despite scoring equally on protective factors such as resilience, mindfulness, and self-compassion. CONCLUSION: The results discussed in this paper suggest that the increased time spent in a virtual learning environment during the COVID-19 pandemic might be associated with burnout among exclusively online learners, as compared to learners who were educated in clinical, in-person settings. Further inquiry should investigate causality and any protective factors that could mitigate negative effects of the virtual learning environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".