Assessing the acceptability of script concordance testing: a nationwide study in otolaryngology
Bibliographic record
Abstract
Background: Script concordance testing (SCT) is an objective method to evaluate clinical reasoning that assesses the ability to interpret medical information under conditions of uncertainty. Many studies have supported its validity as a tool to assess higher levels of learning, but little is known about its acceptability to major stakeholders. The aim of this study was to determine the acceptability of SCT to residents in otolaryngology – head and neck surgery (OTL-HNS) and a reference group of experts. Methods: In 2013 and 2016, a set of SCT questions, as well a post-test exit survey, were included in the National In-Training Examination (NITE) for OTL-HNS. This examination is administered to all OTL-HNS residents across Canada who are in the second to fifth year of residency. The same SCT questions and survey were then sent to a group of OTL-HNS surgeons from 4 Canadian universities. Results: For 64.4% of faculty and residents, the study was their first exposure to SCT. Overall, residents found it difficult to adapt to this form of testing, thought that the clinical scenarios were not clear and believed that SCT was not useful for assessing clinical reasoning. In contrast, the vast majority of experts felt that the test questions reflected real-life clinical situations and would recommend SCT as an evaluation method in OTL-HNS. Conclusion: Views about the acceptability of SCT as an assessment tool for clinical reasoning differed between OTL-HNS residents and experts. Education about SCT and increased exposure to this testing method are necessary to improve residents’ perceptions of SCT.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".