Are rating scales really better than checklists for measuring increasing levels of expertise?
Bibliographic record
Abstract
Background: It is a doctrine that OSCE checklists are not sensitive to increasing levels of expertise whereas rating scales are. This claim is based primarily on a study that used two psychiatry stations and it is not clear to what degree the finding generalizes to other clinical contexts. The purpose of our study was to reexamine the relationship between increasing training and scoring instruments within an OSCE.Approach: A 9-station OSCE progress test was administered to Internal Medicine residents in post-graduate years (PGY) 1–4. Residents were scored using checklists and rating scales. Standard scores from three administrations (27 stations) were analyzed.Findings: Only one station produced a result in which checklist scores did not increase as a function of training level, but the rating scales did. For 13 stations, scores increased as a function of PGY equally for both checklists and rating scales.Conclusion: Checklist scores were as sensitive to the level of training as rating scales for most stations, suggesting that checklists can capture increasing levels of expertise. The choice of which measure is used should be based on the purpose of the examination and not on a belief that one measure can better capture increases in expertise.
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 | 0.135 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".