Increased Student Self-Confidence in Clinical Reasoning Skills Associated with Case-Based Learning (CBL)
Bibliographic record
Abstract
UNLABELLED: Second-year veterinary students were surveyed at the beginning and end of a 15-week semester regarding their confidence in performing three clinical reasoning skills: (1) making Problem Lists; (2) making Rule-Out Lists; and (3) selecting appropriate diagnostic tests. Each week during the semester, these skills were practiced in small-group case discussions. Changes in self-confidence were analyzed and studied in light of faculty assessments of student competence in performance of the three skills. RATIONALE: The purpose of the study was to determine if students' self-confidence in performing three clinical reasoning skills increased with practice. METHODOLOGY: On the first and last days of class, students rated their confidence in each of the three skills on a scale of 0 to 10. Mean confidence scores for the whole class both for time points and for each of the three skills were analyzed. RESULTS: There were significant increases in students' self-confidence in all three clinical reasoning skills over the semester each year. A greater percentage of students expressed improved confidence in selecting appropriate diagnostic tests than in the other two skills in three of the four years studied. CONCLUSIONS: Students' self-confidence in performing three clinical reasoning skills improved over the course of a semester in which they practiced the skills in a CBL format. Subjective faculty assessment of students' competence in these skills generally indicated improvement. However, no meaningful conclusions about the correlation of skill competence and student confidence could be drawn because of inadequacies in the measurement of student performance.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".