Real Patient Participation in Workplace-Based Assessment of Health Professional Trainees: A Scoping Review
Bibliographic record
Abstract
The aim of this scoping review is to outline the existing landscape of how real patients participate in the workplace-based assessment of trainees across diverse healthcare professions. In 2019-2020, the authors searched MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC, and Web of Science databases for studies that included descriptions of experiences whereby patients received care from a health professional trainee and participated in workplace-based assessments of that trainee. Full-text articles published in English from 2009 to 2020 were included in the search. Of the 8770 studies screened; 77 full-text articles were included. Analysis showed that strategies for patient participation in workplace-based assessment varied widely. Aspects studied ranged from validation of an assessment tool to evaluation of the impact of an educational intervention on trainees' performance. Assessment of patient satisfaction was the most common approach to patient involvement. The majority of studies were conducted in North America and in the context of physician training. Formal patient participation in the assessment of health professional trainees appears heterogeneous across health professions. Gaps in the literature are evident; therefore, this review points to an inclusive approach to workplace-based assessment to ensure patient feedback of the trainees who care for them is represented.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".