Factors That Influence Residents' Perceived Credibility of Examiners During a Formative OSCE
Bibliographic record
Abstract
Introduction: Formative assessment requires effective feedback. Recent studies have demonstrated the importance of considering the learner's perspective as a determinant of feedback effectiveness.Objective: We examined factors affecting residents' perceptions of the credibility of their examiners/feedback providers during a formative OSCE, including examiner demographics.Methods: Following a formative 5-station internal medicine OSCE, residents were randomized to receive feedback with or without scores, and then asked to fill a questionnaire rating the credibility of feedback providers on their final station. Feedback was provided by clinical faculty or standardized patients (SPs). Residents also rated the quality and characteristics of the feedback received, and previous knowledge of examiner. Correlations between feedback characteristics were explored and predictors of credibility were examined through multivariable linear regression.Results: 180 residents participated in the OSCE. Mean credibility of examiners was 4.37 of 5 (with 89% ≥ 4). Credibility was somewhat lower for SP raters compared to faculty, but differences were not significant (4.09 versus 4.41, P = .47). Rater and learner demographic variables did not affect credibility on multivariable analysis; however, the following feedback characteristics were significant predictors of credibility: specificity and balance between positive and formative comments. Provision of scores, actual or perceived resident score on station, length of feedback, and pacing did not appear to affect credibility. Perceived quality of feedback was strongly correlated with credibility.Conclusions: Residents rated the credibility of feedback providers highly in this formative OSCE. Characteristics of raters did not affect credibility. Feedback format, including specificity and balance, were associated with higher feedback credibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".