Use of the Script Concordance Approach to Evaluate Clinical Reasoning in Food-Ruminant Practitioners
Bibliographic record
Abstract
A script concordance test (SCT) was developed measuring clinical reasoning of food-ruminant practitioners for whom potential clinical competence difficulties were identified by their provincial professional organization. The SCT was designed to be used as part of a broader evaluation procedure. A scoring key was developed based on answers from a reference panel of 12 experts and using the modified aggregate method commonly used for SCTs. A convenient sample of 29 food-ruminant practitioners was constituted to assess the reliability and precision of the SCT and to determine a fair threshold value for success. Cronbach's α coefficients were computed to evaluate internal reliability. To evaluate SCT precision, a test-retest methodology was used and measures of agreement beyond chance were computed at question and test levels. After optimization, the 36-question SCT yielded acceptable internal reliability (Cronbach's α=0.70). Precision of the SCT at question level was excellent with 33 questions (92%) yielding moderate to almost perfect agreement between administrations. At test level, fair agreement (concordance correlation coefficient=0.32) was observed between administrations. A slight SCT score improvement (M=+2.8 points) on the second administration was in part responsible for some of the disagreement and was potentially a result of an adaptation to the SCT format. Scores distribution was used to determine a fair threshold value for success, while considering the underlying objectives of the examination. The data suggest that the developed SCT can be used as a reliable and precise measurement of clinical reasoning of food-ruminant practitioners.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".