Scoring Methods in Script Concordance Tests: An Exploratory Psychometric Study
Bibliographic record
Abstract
Background: Despite the increasingly popular role of script concordance test (SCT) scoring methods in the evaluation of clinical reasoning, studies examining these methods in nursing are relatively scarce. This study explored the psychometric properties of five SCT scoring methods. Method: An SCT was administered to 12 experts and 43 learners. Scores were calculated using five methods and descriptive statistics. Differences in scores were assessed with the Mann-Whitney U test, and Spearman correlation coefficients were calculated for the different methods. Results: The median scores of both experts and learners differed substantially according to the scoring method used. Learners' scores were statistically different from experts' scores ( p < .01) for each method. Spearman coefficients (range, 0.44 to 0.95) were positive for the different methods. Conclusion: Further research is needed to refine the influence of SCT scoring methods for use in certifying assessment of clinical reasoning in nursing. [ J Nurs Educ . 2023;62(10):549–555.]
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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 | 0.096 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".