Vulnerable Medical Student Ecosystems: Transdisciplinary Learning Sciences Interventions, Maximizing Student Learning and Promoting Mental Health
Bibliographic record
Abstract
Medical students (MS), as a focus of investigation, are usually the last group that would be considered as suffering from mental health issues. However, the literature shows otherwise; MS suffer debilitating anxiety and depression which worsen with the progression of their studies. The literature also highlights the medical school curriculum as a significant cause of the elevated stress, anxiety, and depression levels within the MS population. This article explores the vulnerable nature of MS by focusing on the nature of the medical school’s hidden curriculum and culture, highlighting its impact on the entire medical education ecosystem and the MS. This article, then, investigates the three dominant epistemological belief frames in medical school which impact the vulnerable nature of MS. Finally, this article presents potential interventions, targeting the need for cultural change that may contribute to the creation of a more compassionate learning ecosystem to build the MS’ mental resilience in medical school and create a stronger medical workforce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".