P.132 Investigation of Simulation-Based Lumbar Puncture Teaching Paradigms for Novice Learners
Bibliographic record
Abstract
Background: Simulation-based educations’ prevalence within clinical neuroscience is on the rise, however investigation into what environment is most conducive to optimizing learning performance is limited. We aimed to determine whether training a simple-to-complex (progressive) sequence would result in superior learning compared to complex-to-simple (mixed) or complex-only sequences. Methods: A three-arm, prospective, randomised experiment was conducted to determine the effects on novice learner LP performance and cognitive load during learning and a very complex simulated reality assessment test 9-11 days later. Results: During learning, sterility breaches decreased linearly over time (p<.01) with no group differences, and accuracy was higher in the progressive group compared to complex-only (p<.01) and trended in the mixed group (p<.09). Across the learning phase cognitive load increased in the progressive group (p<.01) and decreased across the mixed group (p<.01). At assessment, there were no group differences in number of sterility breaches (p=.66), needle passes (p=.68) or cognitive load (p=.25). Conclusions: Contrary to our hypothesis, equivocal assessment performance was found across groups. Our results suggest that successive progression in complexity of simulation does not increase novice learner outcomes. Further, a “one-size fits all” approach to simulated environment complexity in clinical neurosciences education may be warranted given equivocal learning and less resources necessary.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".