The effect of simulator training on clinical skills acquisition, retention and transfer
Bibliographic record
Abstract
CONTEXT: Prior research has demonstrated that residents have poor clinical skills in cardiology and respirology. It is not clear how these skills can be improved because the number of patients with suitable clinical findings whose cooperation might help residents to better develop these clinical skills is limited. Objectives Our objective was to evaluate the effect of training on a cardiorespiratory simulator (CRS) on skills acquisition, retention and transfer. METHODS: We randomly allocated 146 students to CRS training in either chest pain or dyspnoea and compared each student's performance on the clinical presentation in which he or she had received CRS training with performance on the control presentation. RESULTS: Immediately after training, students were more accurate in identifying abnormal clinical findings on the CRS (70.0% versus 52.2%; d = 7.6, P < 0.0001) and showed improved diagnostic performance (72.1% versus 55.6%; d = 4.3, P = 0.0007) on the training clinical presentation. At the end of the course they were still better at identifying abnormal findings (57.1% versus 51.7%; d = 2.5, P = 0.004) and diagnosing correctly (50.0% versus 38.1%; d = 3.0, P = 0.002) on problems included in the training clinical presentation. However, they showed no difference between training and control presentations in diagnostic performance when required to transfer their skills between problems (45.9% versus 43.8%; P = 0.5) or in performance on multiple-choice questions (64.1% versus 63.6%; P = 0.8). CONCLUSIONS: Students can acquire and retain clinical skills with CRS training, but demonstrate limited ability to transfer these to other problems. Further studies are needed to explore ways of improving learning and transfer with CRS training.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".