Exploring the impact of the COVID-19 pandemic on medical learner wellness: a needs assessment for the development of learner wellness interventions
Bibliographic record
Abstract
BACKGROUND: On March 11, 2020 the World Health Organization declared the novel coronavirus SARS-CoV-2 disease (COVID-19) a global pandemic. We sought to understand impact of COVID-19 on learner wellness at a large tertiary care academic institution to inform the future development of learner wellness interventions during the COVID-19 pandemic. METHODS: A cross-sectional, internet-based survey collected quantitative and qualitative data from learners April-June 2020. Descriptive statistics and univariate analyses were reported for quantitative data. Open-ended, qualitative responses were analyzed deductively using thematic analysis. RESULTS: Twenty percent of enrolled learners in that faculty of medicine (540/2741) participated including undergraduate [Bachelor's] students (25.7%), graduate [science] students (27.5%), undergraduate medical students (22.8%), and postgraduate resident physicians (23.5%). We found that learner wellness across all stages of training was negatively impacted and the ways in which learners were impacted varied as a result of their program's response to the COVID-19 pandemic. CONCLUSIONS: Learners in health sciences and medical education report worsening well-being because of the programs and the systems in which they function with the added burden of the COVID-19 pandemic. Future interventions would benefit from a holistic framework of learner wellness while engaging in systems thinking to understand how individuals, programs and respective systems intersect. The importance of acknowledging equity, diversity and inclusion, fostering psychological safety and engaging learners as active participants in their journey during a pandemic and beyond are key elements in developing wellness interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".