A Canadian Rural Living Lab Hospital: Implementing solutions for improving rural emergency care
Bibliographic record
Abstract
ABSTRACT Introduction More than 6 million Canadians live in rural areas (approximately 20% of the population) and emergency services are a critical safety net for them. Objectives We want to create, in Baie-Saint-Paul (rural emergency department, Québec, Canada), an experimental milieu where all stakeholders develop, implement and evaluate solutions to address the problems that beset their environment. Method The Living Lab will rely on the quadruple aim approach to improve health system performance and will use a multimethod approach based on the philosophy of open and user-driven innovation. Three pilot projects will be implemented (quality of work life programme, computed tomography implementation study and telemedicine in ambulances). Other possible solutions will be evaluated and prioritised (in situ simulation, care protocol, telemedicine, point-of-care ultrasound, helicopters and drones). Conclusion We are confident that this Living Lab will contribute to saving lives, will improve the quality of work life for rural healthcare professionals, and will inspire similar innovation internationally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".