Identifying Royal College-accredited simulation centre research priorities across Canada
Bibliographic record
Abstract
To advance the field of health sciences simulation, research must be of high quality and would benefit from multi-institutional collaboration where centres can leverage and share expertise as well as work together to overcome limits to the generalizability of research findings from single-institution studies. A needs assessment in emergency medicine simulation has illustrated the importance of identifying research priorities in Canada. The main purpose of this study was to identify simulation research priority directions for Canadian simulation centres. The current survey study drew on 16 research priorities developed through a two-round internal Delphi study at McGill University that 15 of 17 simulation centre advisory board members participated in. The final 16 research priorities were then rated by a total of 18 of 24 simulation centre directors and/or delegates contacted from 15 of 19 Royal College of Physicians and Surgeons of Canada-accredited simulation centres in Canada. Results revealed nine common research priorities that reached 70% or higher agreement for all respondents. We anticipate that our findings can contribute to building a shared vision of priorities, community, and collaboration to enhance health care simulation research quality amongst Canadian simulation centres.
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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 | 0.075 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".