Hockey lines for simulation-based learning
Bibliographic record
Abstract
BACKGROUND: Simulation-based health professional education is often limited in accommodating large numbers of students. Most organisations do not have enough simulation suites or staff to support growing demands. CONTEXT: We needed to find ways to make simulation sessions more accommodating for larger groups of learners, so that more than a few individuals could be active in a simulation scenario at any one time. Moreover, we needed to make the experience meaningful for all participating learners. INNOVATION: We used the metaphor of (ice) hockey lines and substitution 'on the fly' to effectively double the numbers of learners that can be actively engaged at once. Team players must communicate clearly, and observe keenly, so that currently playing members understand what is happening from moment to moment and incoming substitutes can take over their roles seamlessly. Most organisations do not have enough simulation suites or staff to support growing demands IMPLICATIONS: We found that this hockey lines approach to simulation-based team scenarios will raise learners' levels of engagement, reinforce good crew resource management (CRM) practices, enhance closed-loop communication, and help learners to understand their cognitive biases and limitations when working in high-pressure situations. During our continuing refinement of the hockey-lines approach, we developed a number of variations on the basic activity model, with various benefits and applications. Both students and teachers have been enthusiastically positive about this approach when it was introduced at our various courses and participating institutions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".