Bibliographic record
Abstract
PURPOSE: Medical educators have expressed concern that students' professional identities do not always align with their expectations or with professional standards. The authors propose that, in constructing appropriate professional identities, medical students today are affected by the competing discourses of diversity and standardization. METHOD: Between March and May 2012, the authors conducted a critical review of seminal publications to highlight the discourses of diversity and standardization in the medical education literature. They surveyed the social sciences literature on identity construction and drew examples from medical education to demonstrate how a social constructionist approach could inform the discussion about how medical students' professional identities are affected by these discourses. RESULTS: The discourse of diversity emphasizes individuality, difference, and a plurality of possibilities and advances the notion that heterogeneity is beneficial to medical education and to patients. In contrast, the discourse of standardization strives for homogeneity, sameness, and a limited range of possibilities and conveys that there is a single way to be a competent, professional physician. Thus, these discourses are in tension, a fact that medical educators largely have ignored. A social constructionist approach to identity suggests that medical students resolve this tension in different ways and construct different identities as a result. CONCLUSIONS: To influence medical students' professional identity construction, the authors advocate that educators seek change across the profession-faculty must acknowledge and take advantage of the tension between the discourses of standardization and diversity.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".