Being, becoming, and belonging: reconceptualizing professional identity formation in medicine
Bibliographic record
Abstract
Over the last decade, there has been a drive to emphasize professional identity formation in medical education. This shift has had important and positive implications for the education of physicians. However, the increasing recognition of longstanding structural inequalities within society and the profession has highlighted how conceptualizations of professional identity formation have also had unintended harmful consequences. These include experiences of identity threat and exclusion, and the promotion of norms and values that over-emphasize the preferences of culturally dominant groups. In this paper, the authors put forth a reconceptualization of the process of professional identity formation in medicine through the elaboration of 3 schematic representations. Evolutions in the understandings of professional identity formation, as described in this paper, include re-defining socialization as an active process involving critical engagement with professional norms, emphasizing the role of agency, and recognizing the importance of belonging or exclusion on one's sense of professional self. The authors have framed their analysis as an evidence-informed educational guide with the aim of supporting the development of identities which embrace diverse ways of being, becoming, and belonging within the profession, while simultaneously upholding the standards required for the profession to meet its obligations to patients and society.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".