Assessment Tools for Feedback and Entrustment Decisions in the Clinical Workplace: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Entrustable Professional Activities (EPAs) combine feedback and evaluation with a permission to act under a specified level of supervision and the possibility to schedule learners for clinical service. This literature review aims to identify workplace-based assessment tools that indicate progression toward unsupervised practice, suitable for entrustment decisions and feedback to learners. METHODS: A systematic search was performed in the PubMed, Embase, ERIC, and PsycINFO databases. Based on title/abstract and full text, articles were selected using predetermined inclusion and exclusion criteria. Information on workplace-based assessment tools was extracted using data coding sheets. The methodological quality of studies was assessed using the medical education research study quality instrument (MERSQI). RESULTS: The search yielded 6,371 articles (180 were evaluated in full text). In total, 80 articles were included, identifying 67 assessment tools. Only a few studies explicitly mentioned assessment tools used as a resource for entrustment decisions. Validity evidence was frequently reported, and the MERSQI score was 10.0 on average. CONCLUSIONS: Many workplace-based assessment tools were identified that potentially support learners with feedback on their development and support supervisors with providing feedback. As expected, only few articles referred to entrustment decisions. Nevertheless, the existing tools or the principals could be used for entrustment decisions, supervision level, or autonomy.
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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 | 0.025 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".