Medical student reporting of factors affecting pre-clerkship changes in empathy: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: To isolate factors that medical students identify as possibly affecting empathy in pre-clerkship years of medical school. METHODS: 12 students in their second year of medical school at Queen's University were randomly selected and asked to participate in semi-structured interviews conducted from an ethnographic perspective. RESULTS: Students reported both negative and positive changes in empathy. Negative changes included desensitization and focusing on the disease process, decreased ability to see things from patients' perspectives, and routine responses in emotional situations. These changes occur due to time constraints, objective lessons in empathy, and a changing identity. Positive changes included an increased awareness of the impact of illness, and increased ability to read feelings. These changes result from increased exposure to patients, discussions surrounding the psychosocial impact of illness, and positive role models. CONCLUSION: Students should be made aware of the limitations of objective lessons in empathy, and non-evaluated, implicit lessons should be emphasized when possible. Students should be encouraged to maintain relationships outside of medicine. Aspects of medical school that currently promote empathy should be reinforced, including exposure to patients, opportunities to work closely with positive role models, and practical discussions surrounding the psychosocial impact of illness.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".