Emotional Learning and Identity Development in Medicine: A Cross-Cultural Qualitative Study Comparing Taiwanese and Dutch Medical Undergraduates
Bibliographic record
Abstract
PURPOSE: Current knowledge about the interplay between emotions and professional identity formation is limited and largely based on research in Western settings. This study aimed to broaden understandings of professional identity formation cross-culturally. METHOD: In fall 2014, the authors purposively sampled 22 clinical students from Taiwan and the Netherlands and asked them to keep audio diaries, narrating emotional experiences during clerkships using three prompts: What happened? What did you feel/think/do? How does this interplay with your development as a doctor? Dutch audio diaries were supplemented with follow-up interviews. The authors analyzed participants' narratives using a critical discourse analysis informed by Figured Worlds theory and Bakhtin's concept of dialogism, according to which people's spoken words create identities in imagined future worlds. RESULTS: Participants talked vividly, but differently, about their experiences. Dutch participants' emotions related to individual achievement and competence. Taiwanese participants' rich, emotional language reflected on becoming both a good person and a good doctor. These discourses constructed doctors' and patients' autonomy in culturally specific ways. The Dutch construct centered on "hands-on" participation, which developed the identity of a technically skilled doctor, but did not address patients' self-determination. The Taiwanese construct located physicians' autonomy within moral values more than practical proficiency, and gave patients agency to influence doctor-patient relationships. CONCLUSIONS: Participants' cultural constructs of physician and patient autonomy led them to construct different professional identities within different imagined worlds. The contrasting discourses show how medical students learn about different meanings of becoming doctors in culturally specific 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".