Validation of a Scenario-Based Assessment of Critical Thinking Using an Externally Validated Tool
Bibliographic record
Abstract
With medical education transitioning from knowledge-based curricula to competency-based curricula, critical thinking skills have emerged as a major competency. While there are validated external instruments for assessing critical thinking, many educators have created their own custom assessments of critical thinking. However, the face validity of these assessments has not been challenged. The purpose of this study was to compare results from a custom assessment of critical thinking with the results from a validated external instrument of critical thinking. Students from the College of Veterinary Medicine at Western University of Health Sciences were administered a custom assessment of critical thinking (ACT) examination and the externally validated instrument, California Critical Thinking Skills Test (CCTST), in the spring of 2011. Total scores and sub-scores from each exam were analyzed for significant correlations using Pearson correlation coefficients. Significant correlations between ACT Blooms 2 and deductive reasoning and total ACT score and deductive reasoning were demonstrated with correlation coefficients of 0.24 and 0.22, respectively. No other statistically significant correlations were found. The lack of significant correlation between the two examinations illustrates the need in medical education to externally validate internal custom assessments. Ultimately, the development and validation of custom assessments of non-knowledge-based competencies will produce higher quality medical professionals.
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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 | 0.031 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".