Coordinating Progressive Levels of Simulation Fidelity to Maximize Educational Benefit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of a novel, simulation-based educational model rooted in scaffolding theory that capitalizes on a systematic progressive sequence of simulators that increase in realism (i.e., fidelity) and information content. METHOD: Forty-five medical students were randomly assigned to practice intravenous catheterization using high-fidelity training, low-fidelity training, or progressive training from low to mid to high fidelity. One week later, participants completed a transfer test on a standardized patient simulation. Blinded expert raters assessed participants' global clinical performance, communication, procedure documentation, and technical skills on the transfer test. Participants' management of the resources available during practice was also recorded. Data were analyzed using multivariate analysis of variance. The study was conducted in fall 2008 at the University of Toronto. RESULTS: The high-fidelity group scored higher (P < .05) than the low-fidelity group on all measures except procedure documentation. The progressive group scored higher (P < .05) than other groups for documentation and global clinical performance and was equivalent to the high-fidelity group for communication and technical skills. Total practice time was greatest for the progressive group; however, this group required little practice time on the resource-intensive high-fidelity simulator. CONCLUSIONS: Allowing students to progress in their practice on simulators of increasing fidelity led to superior transfer of a broad range of clinical skills. Further, this progressive group was resource-efficient, as participants concentrated on lower fidelity and lower resource-intensive simulators. It is suggested that clinical training curricula incorporate exposure to multiple simulators to maximize educational benefit and potentially save educator time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it