Unveiling the shadows: A qualitative inquiry into burnout perceptions among pre-clerkship students in Canadian medical education
Bibliographic record
Abstract
Emerging evidence indicates that burnout manifests early, even among medical students. The pre-clerkship demographic remains inadequately understood with a notable scarcity of studies examining burnout. To address this gap, this project developed a conceptual model to better understand burnout among pre-clerkship = students. Fifteen second-year medical students participated in interviews exploring their experiences with educational stress and perceptions of burnout. Constructivist Grounded Theory informed data collection and analysis due to the undertheorized nature of burnout in the pre-clerkship setting. Recurring themes encompassed information overload, perceived limited faculty support, anticipatory stress of entering clerkship and social isolation during hybrid learning, highlighting multifaceted stressors that ranged from academic to relational. Participants underscored various personal factors, such as time management, adaptation strategies and focusing on personal strengths, as mitigating burnout. Participants recognized the systemic nature of burnout, suggesting that early clinical exposure, peer mentorship and social connections, and dedicated personal time could serve as protective measures. This study addresses a gap in the literature by presenting a model of how personal characteristics interact with institutional and environmental pressures among pre-clerkship students. The findings prompt reflection on why pre-clerkship students express burnout before entering the clinical setting, advocating for institutional strategies to support junior learners.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".