I Read, Therefore I Am: Examining Nonmedical Reading and Its Relationship to Empathy in Anesthesia Training
Bibliographic record
Abstract
Background: High levels of empathy among resident physicians are associated with improved patient outcomes. Empathy may be learned and practiced when reading nonmedical writing through narrative transportation, a process by which readers identify with characters and become emotionally involved in the plot. We hypothesized that residents and fellows who reported more nonmedical reading would have higher empathy levels and that empathy would decrease during training. Methods: An emailed survey was sent to program directors of Accreditation Council on Graduate Medical Education-accredited anesthesiology residency and fellowship programs, with a request to distribute the survey to trainees. The Toronto Empathy Questionnaire, reading volume, and demographics were included in the survey. Response data were analyzed using a multiple variable regression model. Results: = .039). Age, postgraduate year of training, relationship status, time spent with family, and avid reading were not significantly associated with increased empathy. Conclusion: In this study, we examined whether nonmedical fiction reading would increase empathy in medical trainees. Our study was not able to find any significant association with time spent reading and increased empathy; however, we found that trainees who worked more hours, specifically 60 to 80 hours, had higher empathy scores. Limitations for this study included a smaller sample size. Further research should be done in this field to determine if there are other intangible factors that affect empathy in trainees.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".