A Critical Review of Professional Identity Formation Interventions in Medical Education
Bibliographic record
Abstract
PURPOSE: Professional identity formation (PIF) can be defined as the integration of the knowledge, skills, values, and behaviors of a profession with one's preexisting identity and values. Several different, and sometimes conflicting, conceptualizations and theories about PIF populate the literature; applying these different theories in PIF curricula and pedagogic strategies can profoundly impact the PIF of future physicians. The authors conducted a critical review of the recent literature on PIF interventions in medical education to explore the conceptualizations of and theoretical approaches to PIF that underlie them. METHOD: The authors searched articles on PIF educational interventions published in 5 major medical education journals between 2010 and March 2021. The articles' context and findings were extracted, analyzed, and summarized to identify conceptualizations and theoretical approaches to PIF. RESULTS: The authors identified 43 studies examining medical education interventions aimed at influencing PIF. The majority of the studies (n = 31) focused on undergraduate medical education. Reflective writing and the use of narrative reflections were the dominant modes of student activity in PIF interventions, supporting the dominant individualist approach to PIF. Less commonly PIF was understood as a socialization process or as an active process with both individually and socially focused influences. CONCLUSIONS: Relying on reflective writing as the intervention of choice to impact PIF feeds the dominant individualist perspective on PIF. An unintended consequence of this individualist orientation is that cultural problems embedded in the profession can become burdens for individual physicians to personally bear. Future education and research into PIF should account for theoretical preferences and the impact of these preferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".