Use of Cognitive Aids: Results from a National Survey among Anaesthesia Providers in France and Canada
Bibliographic record
Abstract
INTRODUCTION: The use of cognitive aids (CAs) during critical events is thought to be useful. However, whether CAs are known and used by French and Canadian anaesthesia providers is not clear. METHODS: A survey was emailed to French and Canadian anaesthesia providers in 2017 through their respective national societies. It consisted of 23 questions about the participants' demographics and their knowledge, use, and impact of CAs. A second survey was sent to French simulation centres. RESULTS: 912 responses were recorded in France and 278 in Canada (overall response rate: 7% and 11%, respectively). Among the respondents, 700/899 in France (78%) versus 249/273 (91%) in Canada were familiar with the concept of cognitive dysfunction during a crisis and 501/893 (56%) in France versus 250/271 (92%) in Canada knew the concept of CAs. Amongst those respondents who knew about CAs, 189/492 (38%) in France versus 108/244 (44%) in Canada stated that they had already used a CA in real life and 225/493 (45%) in France versus 126/245 (51%) in Canada had received training in their use. Simulation was the principal modality for training in 150/225 (67%) of cases in France versus 47/126 (37%) in Canada. Among the 28/50 French simulation centres which responded (2018 January), 27 organised sessions in anaesthesia and 22 used CAs. CONCLUSION: CAs were better known in Canada than in France, but their actual use in real life was low in both countries. Simulation appears to play a potentially important role training anaesthesia providers in the use of CAs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".