Do Medical Students’ Narrative Representations of “The Good Doctor” Change Over Time? Comparing Humanism Essays From a National Contest in 1999 and 2013
Bibliographic record
Abstract
PURPOSE: To explore medical students' conceptions of "the good doctor" at two points in time separated by 14 years. METHOD: The authors conducted qualitative analysis of narrative-based essays. Following a constant comparative method, an emergent relational coding scheme was developed which the authors used to characterize 110 essays submitted to the Arnold P. Gold Foundation Humanism in Medicine Essay Contest in 1999 (n = 50) and 2013 (n = 60) in response to the prompt, "Who is the good doctor?" RESULTS: The authors identified five relational themes as guiding the day-to-day work and lives of physicians: doctor-patient, doctor-self, doctor-learner, doctor-colleague, and doctor-system/society/profession. The authors noted a highly similar distribution of primary and secondary relational themes for essays from 1999 and 2013. The majority of the essays emphasized the centrality of the doctor-patient relationship. Student essays focused little on teamwork, systems innovation, or technology use-all important developments in contemporary medicine. CONCLUSIONS: Medical students' narrative reflections are increasingly used as rich sources of information about the lived experience of medical education. The findings reported here suggest that medical students understand the "good doctor" as a relational being, with an enduring emphasis on the doctor-patient relationship. Medical education would benefit from including an emphasis on the relational aspects of medicine. Future research should focus on relational learning as a pedagogical approach that may support the formation of caring, effective physicians embedded in a complex array of relationships within clinical, community, and larger societal contexts.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| 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".