Patient Engagement in Medical Trainee Selection: A Scoping Review
Bibliographic record
Abstract
PURPOSE: The stakes of medical trainee selection are high, making it ironic and somewhat paradoxical that patients and the public often get little say in selection practices. The authors sought to undertake a knowledge synthesis to uncover what is known about patient engagement across the medical trainee selection continuum. METHOD: The authors conducted a scoping review aimed at exploring the current state of practice and research on patient engagement in medical trainee selection in 2017-2021. MeSH headings and keywords were used to capture patient, community, and standardized patient engagement in selection processes across multiple health professions. The authors employed broad inclusion criteria and iteratively refined the corpus, ultimately, limiting study selection to those reporting engagement of actual patients in selection within medicine, but maintaining a broad focus on any patient contributions across the entire selection continuum. The Cambridge Framework was adapted and used to organize the included studies. RESULTS: In total, 2,858 abstracts were reviewed, and ultimately, 28 papers were included in the final corpus. The included studies were global but nascent. Most of the literature on this topic appears in the form of individual projects advocating for patient engagement in selection rather than cohesive programs with empirical exploration of patient engagement in selection. Job analysis methodology was particularly prominent for incorporating the patient voice into identifying competencies of relevance to selection. Direct patient engagement in early selection activities allowed the patient voice to assist candidates in determining their fit for medicine. CONCLUSIONS: Patient engagement has not been made a specific focus of study in its own right, leading the authors to encourage researchers to turn their lens more directly on patient engagement to explore how it complements the professional voice in medical trainee selection.
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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 | 0.044 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".