Resilience, stress, and coping among Canadian medical students
Bibliographic record
Abstract
BACKGROUND: Numerous studies have established that medical school is a stressful place but coping styles and resilience have not been adequately addressed as protective factors. METHOD: Using a cross-sectional design, 155 students were surveyed using the Connor-Davidson Resilience Scale, Perceived Stress Scale, and the Canadian Community Health Survey Coping Scale. Mean scores were compared by gender and between our sample and normative scores using t-tests. Multivariate linear regression was performed to examine whether stress levels were related to coping and resilience. RESULTS: Medical students had higher perceived stress, negative coping, and lower resilience than age and gender-matched peers in the general population. Male medical students had higher positive coping scores than general population peers and higher resilience, and lower perceived stress than female medical students. Coping scores did not vary by gender in our sample. The multivariate model showed that resilience and negative, but not positive coping, predicted stress. CONCLUSIONS: Medical students are neither more resilient nor better equipped with coping skills than peers in the population. Greater emphasis on self-care among medical trainees is recommended. Emphasizing the importance of self-care during medical training, whether by formal incorporation into the curriculum or informal mentorship, deserves further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".