Supporting Medical Student Wellness During a Pandemic: A Pilot Study of an Extra-Curricular Resilience-Promotion Program
Bibliographic record
Abstract
Background: Medical students face a unique collection of stressors, both intrinsic and external with consequences to health and well-being, ultimately impacting patient care. Health education institutions play a role in perpetuating or mitigating this experience of stress. Objective: This pilot project aimed to identify the impact of an extra-curricular wellness program on well-being among naturopathic medical students. Methods: Participants engaged in a 2-day facilitated wellness retreat followed by three monthly workshops, which coincided with the onset of the COVID-19 pandemic. Frequencies of pre-defined behaviours, and validated measures of stress, resilience, and mental health were assessed prior to the start of the program, immediately upon completion, and 6 months after the program concluded and were compared with wait-listed controls. Results: There was a clear trend toward reduced stress perception and anxiety, along with improved resilience among program participants through the duration of the study and compared with controls. The positive trends over the course of the study, especially during a global pandemic, suggests that the intervention was beneficial to participants. Conclusion: The results suggest that supporting students in cultivating the skills of resilient coping may reduce perceived stress and improve mental health for medical students, even during times of uncontrollable external stress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".