CTSim: Changing teaching practice in radiography with simulation
Bibliographic record
Abstract
INTRODUCTION: Simulation offers radiography students the possibility to experiment with Computed Tomography (CT) in a way not possible in clinical practice. The aim of this work was to test a newly developed simulator 'CTSim' for effectiveness in teaching and learning. METHODS: The simulator was tested in two phases. The first phase used a test-retest methodology with two groups, a group that experienced a Simulation based learning intervention and one which did not. The second phase subsequently tested for changes when the same intervention was introduced as part of an existing CT training module. RESULTS: Phase 1 demonstrated statistically significant improvement of mean scores from 58% to 68% (P < .05) for students who experienced the intervention against no change in scores for the control group. Phase 2 saw mean scores improve statistically significantly in a teaching module from 66% to 73% (P < .05) following the application of the intervention as an active learning component. CONCLUSION: The use of the CTSim simulator had a demonstrable effect on student learning when used as an active learning component in CT teaching. IMPLICATIONS FOR PRACTICE: Simulation tools have a place in enhancing teaching and learning in terms of effectiveness and also introduce variety in the medium by which this is done.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".