A scoping review of Fit in medical education: a guaranteed success, or a threat to inclusivity?
Bibliographic record
Abstract
Background: Finding applicants that fit in with educational environments is a goal of many educators in hopes that it will lead to successful training. "Fit" is used colloquially to describe a general feeling, however the field of study has grown to include specific terms describing the compatibility between people and their environments, organizations, and jobs. Despite common use, the term is used often but non-specifically in medical education. This review aims to examine the current literature of fit in medical education, how fit is defined, measured and whether it correlates to educational outcomes. Methods: A systematic database search was conducted in 2024 with Medline, Embase, APA PsychINFO, ERIC and Education Source from 1970 to April 23, 2024. Key search terms included fit, student, medicine, clinical, education. Relevant data included definitions of fit, measurement tools, and correlation with educational outcomes. The standard six-step scoping review framework and PRISMA-ScR reporting guidelines were used. Results: The search identified 1960 non-duplicate articles, 11 of which were included in the review after screening. Fit was specifically defined in only three articles and was measured primarily through personality and value testing with interviews and surveys. Educational outcomes correlated positively with fit, however were studied in just three articles. Conclusions: Person-organization fit may correlate positively with medical education outcomes however there is limited research in this field. Further research should explore methods in evaluating for fit in trainee selection while focusing on the risk of discrimination based on intrinsic biases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.308 | 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 teacher head, 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".