Wellness in medical education: definition and five domains for wellness among medical learners during the COVID-19 pandemic and beyond
Bibliographic record
Abstract
Problem: The novel coronavirus SARS-CoV-2 disease (COVID-19) impacted medical learner well-being and serves as a unique opportunity to understand medical learner wellness. The authors designed a formal needs assessment to assess medical learners’ perspectives regarding distress related to disrupted training environments. This Rapid Communication describes findings from a qualitative study which defined medical learner wellness and validated five wellness domains.Approach: We conducted follow-up telephone interviews to an online needs assessment survey to identify a learner definition for wellness and to validate five wellness domains, including social, mental, physical, intellectual, and occupational wellness. Using purposive and maximal variation sampling, 27 students were interviewed from July–August 2020. Thematic analysis was performed using a deductive thematic approach to qualitative analysis.Outcomes: Medical learners defined wellness as a general [holistic] sense of personal well-being – the opportunity to be and to do what they most need and value. Learners validated all five wellness domains for medical education. Learners acknowledged the need for an adoptable and adaptable holistic framework for wellness in medical education.Next steps: We recommend academic medical institutions consider learner wellness a key component of medical education to cultivate learners as a competent collective of self-reliant, scholarly experts. We encourage evaluation of wellness domains in diverse medical learner populations to identify feasible interventions potentially associated with improvements in medical learner wellness.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".