The Influence of Prior Performance Information on Ratings of Current Performance and Implications for Learner Handover: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Learner handover (LH) is the sharing of information about trainees between faculty supervisors. This scoping review aimed to summarize key concepts across disciplines surrounding the influence of prior performance information (PPI) on current performance ratings and implications for LH in medical education. METHOD: The authors used the Arksey and O'Malley framework to systematically select and summarize the literature. Cross-disciplinary searches were conducted in six databases in 2017-2018 for articles published after 1969. To represent PPI relevant to LH in medical education, eligible studies included within-subject indirect PPI for work-type performance and rating of an individual current performance. Quantitative and thematic analyses were conducted. RESULTS: Of 24,442 records identified through database searches and 807 through other searches, 23 articles containing 24 studies were included. Twenty-two studies (92%) reported an assimilation effect (current ratings were biased toward the direction of the PPI). Factors modifying the effect of PPI were observed, with larger effects for highly polarized PPI, negative (vs positive) PPI, and early (vs subsequent) performances. Specific standards, rater motivation, and certain rater characteristics mitigated context effects, whereas increased rater processing demands heightened them. Mixed effects were seen with nature of the performance and with rater expertise and training. CONCLUSIONS: PPI appears likely to influence ratings of current performance, and an assimilation effect is seen with indirect PPI. Whether these findings generalize to medical education is unknown, but they should be considered by educators wanting to implement LH. Future studies should explore PPI in medical education contexts and real-world settings.
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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 | 0.045 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".