Comparison between medical students' experience, confidence and competence
Bibliographic record
Abstract
OBJECTIVES: This study was undertaken to determine whether or not breadth of clinical experience and student levels of confidence were indicators of competency on standardized simulator performance-based assessments. METHODS: All students (n=144) attending an educational session were asked to complete a 25-point questionnaire regarding specific clinical experiences and levels of confidence in their ability to manage patient problems. For enumeration of clinical experiences, students were asked to estimate the number of times a situation had been encountered or a skill had been performed. For level of confidence, each response was based on a 5-point Likert scale where 1=novice and 5=expert. Students then participated in a standardized simulated performance test. Median and range were calculated and data analysed using Spearman rank correlations. A P-value <0.05 was considered significant. Level of confidence data were compared to performance during clinical rotation and to marks in the anaesthesia final examination. RESULTS: A total of 144 students attended the session, completed the questionnaire and participated in the standardized test. There were wide ranges of experience and confidence in the 25 listed items. Analysis of data showed good correlation between clinical experience and level of confidence. There was no correlation between clinical experience, level of confidence and performance in a standardized simulation test. Neither was there any correlation between level of confidence and clinical grades or written examination marks. CONCLUSIONS: Clinical experience and level of confidence have no predictive value in performance assessments when using standardized anaesthesia simulation scenarios.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".