The implementation and evaluation of an e-Learning training module for objective structured clinical examination raters in Canada
Bibliographic record
Abstract
Improving the reliability and consistency of objective structured clinical examination (OSCE) raters' marking poses a continual challenge in medical education. The purpose of this study was to evaluate an e-Learning training module for OSCE raters who participated in the assessment of third-year medical students at the University of Ottawa, Canada. The effects of online training and those of traditional in-person (face-to-face) orientation were compared. Of the 90 physicians recruited as raters for this OSCE, 60 consented to participate (67.7%) in the study in March 2017. Of the 60 participants, 55 rated students during the OSCE, while the remaining 5 were back-up raters. The number of raters in the online training group was 41, while that in the traditional in-person training group was 19. Of those with prior OSCE experience (n= 18) who participated in the online group, 13 (68%) reported that they preferred this format to the in-person orientation. The total average time needed to complete the online module was 15 minutes. Furthermore, 89% of the participants felt the module provided clarity in the rater training process. There was no significant difference in the number of missing ratings based on the type of orientation that raters received. Our study indicates that online OSCE rater training is comparable to traditional face-to-face orientation.
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 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 | 0.043 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".