Contribution of Basic Science Education to the Professional Identity Development of Medical Learners: A Critical Scoping Review
Bibliographic record
Abstract
PURPOSE: Professional identity development (PID) has become an important focus of medical education. To date, contributions of basic science education to physician PID have not been broadly explored. This review explores the literature surrounding the contribution of basic science education to the PID of medical learners and interprets findings critically in terms of the landscapes of practice (LoP) framework. METHOD: In this critical scoping review, the authors searched 12 different databases and professional organization websites from January 1988 to October 2022 for references relating to how, if at all, the basic science component of medical education contributes to the PID of medical learners. The LoP learning theory was chosen as a framework for critically interpreting the identified articles. RESULTS: Of the 6,674 identified references, 257 met the inclusion criteria. After data extraction, content analysis of recorded key findings was used to ensure all findings were incorporated into the synthesis. Findings aligned with and were critically interpreted in relation to the 3 LoP modes of identification: engagement (engaging in the work of a physician), imagination (imagining oneself becoming a "good doctor"), and alignment (aligning with the practices and expectations of a medical community or specialty). Within each mode of identification, it was possible to see how basic science may support, or catalyze, PID and how basic science may serve as a barrier, or an inhibitor, to PID or contribute to the development of negative aspects of identity development. CONCLUSIONS: The LoP learning theory suggests that the effect of basic science on physicians' PID is most effective if educators view themselves as guides through interfaces between their scientific disciplines and medicine. Learners need opportunities to be engaged, to imagine how their current learning activities and developing skills will be useful as future physicians, and to feel alignment with medical specialties.
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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 | 0.054 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| 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".