How medical students cope with stress: a cross-sectional look at strategies and their sociodemographic antecedents
Bibliographic record
Abstract
BACKGROUND: Medical training can be highly stressful for students and negatively impact their mental health. Important to this matter are the types of coping strategies (and their antecedents) medical students use, which are only characterized to a limited extent. A better understanding of these phenomena can shed additional light on ways to support the health and well-being of medical students. Accordingly, we sought to determine medical students' use of various coping reactions to stress and how their gender and year of study influence those behaviours. METHODS: A total of 400 University of Saskatchewan medical students were invited to complete an online survey. Using the Brief COPE inventory, we assessed students' reported use of various adaptive and maladaptive coping strategies. Descriptive and comparative statistics were performed, including multivariate analysis of variance, to explore how gender and year influenced coping strategies. RESULTS: The participation rate was 49% (47% males and 53% females). Overall, the students' coping strategies were mostly adaptive, albeit with a few exceptions. Females used more behavioural disengagement, while males used less emotional and instrumental support. Additionally, third years used more denial to cope with stress than students in any other year. CONCLUSIONS: While few studies report significant sociodemographic effects on medical student coping, our findings raise the possibility that males and females do engage in different coping strategies in medical school, and that the clinical learning environment in third year may provoke more dysfunctional coping, compared to pre-clinical stages of training. Potential explanations and implications of these results are discussed.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".