Bibliographic record
Abstract
BACKGROUND: Efforts to "rehumanize" medical education through curriculum reform and program development have been numerous and ongoing in recent years. One particularly intriguing contribution has come from the area of narrative studies. It is now common to use literature in general, and physician--patient narratives in particular, both to expand students' understanding of the clinical encounter and to sensitize them to the humanistic aspects of medicine. DESCRIPTION: In this article, we describe the process by which we have introduced key insights from and elements of narrative theory into our 1st-year clinical skills program. Rather than limiting our efforts to the use of literature and to the description of individual narrative encounters, however, we have framed our entire course as an exercise in narrative construction. We refer to this process as "narrative structuring." EVALUATION: A combination of short essays on topics related to the various literary materials utilized in the course, written reports on ethical aspects of the clinical cases presented in the clinical skills sessions, and student journaling are integral components of the evaluation of this course. CONCLUSION: Characterizing our course in terms of narrative structuring serves both to integrate the various elements of our complex curriculum around a common theme and to remind both students and faculty alike of the privileges and responsibilities we share as we participate in the writing of one another's stories.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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".