On line clinical reasoning assessment with Script Concordance test in urology: results of a French pilot study
Bibliographic record
Abstract
BACKGROUND: The Script Concordance test (SC) test is an assessment tool that measures the capacity to solve ill-defined problems, that is, reasoning in a context of uncertainty. This study assesses the feasibility, reliability and validity of the SC test made available on the Web to French urologists. METHODS: A 97 items SC test was developed based on major educational objectives of French urology training programmes. A secure Web site was created with two sequential modules: a) The first one for the reference panel to elaborate the scoring system; b) The second for candidates with different levels of experience in urology: Board certified urologists, chief-residents, residents, medical students. All participants were recruited on a voluntary basis. Statistical analysis included descriptive statistics of the participants' scores and factorial analysis of variance (ANOVA) to study differences between groups' means. Reliability was evaluated with Cronbach's alpha coefficient. RESULTS: The on line SC test has been operational since June 2004. Twenty-six faculty members constituted the reference panel. During the following 10 months, 207 participants took the test online (124 urologists, 29 chief-residents, 38 residents, 16 students). No technical problem was encountered. Forty-five percent of the participants completed the test partially only. Differences between the means scores for the 4 groups were statistically significant (P = 0.0123). The Bonferroni post-hoc correction indicated that significant differences were present between students and chief-residents, between students and urologists. There were no differences between chief-residents and urologists. Reliability coefficient was 0.734 for the total group of participants. CONCLUSION: Feasibility of Web-based SC test was proved successful by the large number of participants who participated in a few months. This Web site has permitted to quickly confirm reliability of the SC test and develop strategy to improve construct validity of the test when applied in the field of urology. Nevertheless, optimisation of the SC test content, with a smaller number of items will be necessary. Virtual medical education initiative such as this SC test delivered on the Internet warrants consideration in the current context of national pre-residency certification examination in France.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".