Medical students as whole persons – tending to the elephants in clinical practice training
Bibliographic record
Abstract
BackgroundFor years, we have known that many medical students lose empathy and experience burn out during the last part of their undergraduate education, despite starting with high motivation and above average mental health. The most powerful learning environment is the clinic, where students in the final stages of their program interact with real patients and practice doctor’s skills in authentic environments. We wondered how students at this stage are cared for as learners and novice professionals. We tried to identify explicit and hidden professional norms and competence goals that students are measured by, and sanctioned for not conforming with, in daily practice. We asked: Is there a mismatch between what medical students need to manage in their professional lives and the affordances inherent to the workplace environment where learning takes place? Can we intervene to mitigate any gaps? MethodInspired by the Consolidated Framework for Implementation Research (CFIR), we engaged leaders, physicians, residents, and medical students at a small Norwegian hospital in a three-year project aiming to improve students’ motivation, participation, and clinical learning, by strengthening pedagogical and affective support during an 8-week practice period. ResultsMedical students and residents identified needs for preparation and orientation, continuity, and secure relationships where learners are acknowledged as unique individuals. A simple model of learning needs was developed, where educational goals can be arranged on three levels: 1) social survival, 2) medical knowledge and skills, and 3) clinical wisdom.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".