Using Item Analysis to Assess Objectively the Quality of the Calgary-Cambridge OSCE Checklist
Bibliographic record
Abstract
Background: The purpose of this study was to investigate the use of item analysis to assess objectively the quality of items on the Calgary-Cambridge Communications OSCE checklist. Methods: A total of 150 first year medical students were provided with extensive teaching on the use of the Calgary-Cambridge Guidelines for interviewing patients and participated in a final year end 20 minute communication OSCE station. Grouped into either the upper half (50%) or lower half (50%) communication skills performance groups, discrimination, difficulty and point biserial values were calculated for each checklist item. Results: The mean score on the 33 item communication checklist was 24.09 (SD = 4.46) and the internal reliability coefficient was ? = 0.77. Although most of the items were found to have moderate (k = 12, 36%) or excellent (k = 10, 30%) discrimination values, there were 6 (18%) identified as ‘fair’ and 3 (9%) as ‘poor’. A post-examination review focused on item analysis findings resulted in an increase in checklist reliability (? = 0.80). Conclusions: Item analysis has been used with MCQ exams extensively. In this study, it was also found to be an objective and practical approach to use in evaluating the quality of a standardized OSCE checklist.
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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 | 0.044 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".