Transforming self-experienced vulnerability into professional strength: a dialogical narrative analysis of medical students’ reflective writing
Bibliographic record
Abstract
Medical students' efforts to learn person-centered thinking and behavior can fall short due to the dissonance between person-centered clinical ideals and the prevailing epistemological stereotypes of medicine, where physicians' life events, relations, and emotions seem irrelevant to their professional competence. This paper explores how reflecting on personal life experiences and considering the relevance for one's future professional practice can inform first-year medical students' initial explorations of professional identities. In this narrative inquiry, we undertook a dialogical narrative analysis of 68 essays in which first-year medical students reflected on how personal experiences from before medical school may influence them as future doctors. Students wrote the texts at the end of a 6-month course involving 20 patient encounters, introduction to person-centered theory, peer group discussions, and reflective writing. The analysis targeted medical students' processes of interweaving and delineating personal and professional identities. The analysis yielded four categories. (1) How medical students told their stories of illness, suffering, and relational struggles in an interplay with context that provided them with new perspectives on their own experiences. Students formed identities with a person-centered orientation to medical work by: (2) recognizing and identifying with patients' vulnerability, (3) experiencing the healing function of sharing stories, and (4) transforming personal experiences into professional strength. Innovative approaches to medical education that encourage and support medical students to revisit, reflect on, and reinterpret their emotionally charged life experiences have the potential to shape professional identities in ways that support person-centered orientations to medical work.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| 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".