How do cognitive processes influence script concordance test responses?
Bibliographic record
Abstract
INTRODUCTION: The script concordance test (SCT) is a test of clinical decision-making (CDM) that compares the thought process of learners to that of experts to determine to what extent their cognitive 'scripts' align. Without understanding test-takers' cognitive process, however, it is unclear what influences their responses. The objective of this study was to gather response process validity evidence by studying the cognitive process of test-takers to determine whether the SCT tests CDM and what cognitive processes may influence SCT responses. METHODS: Cases from an SCT used in a national validation study were administered and semi-structured cognitive interviews were conducted with ten residents and five staff surgeons. A retrospective verbal probing technique was used. Data was independently analysed and coded by two analysts. Themes were identified as factors that influence SCT responses during the cognitive interview. RESULTS: Cognitive interviews demonstrated variability in CDM among test-takers. Consistent with dual process theory, test-takers relied on scripts formed through past experiences, when available, to make decisions and used conscious deliberation in the absence of experience. However, test-takers' response process was also influenced by their comprehension of specific terms, desire for additional information, disagreement with the planned management, underlying knowledge gaps and desire to demonstrate confidence or humility. CONCLUSION: The rationale behind SCT answers may be influenced by comprehension, underlying knowledge and social desirability in addition to formed scripts and/or conscious deliberation. Having test-takers verbalise their rationale for responses provides a depth of assessment that is otherwise lost in the SCT's current format. With the improved ability to standardise CDM assessment using the SCT, consideration of test-makers improving the SCT construction process and combining the SCT question format with verbal responses may improve the use of the SCT for CDM assessment.
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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 | 0.500 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".