The process flow and structure of an integrated stroke strategy
Bibliographic record
Abstract
INTRODUCTION: In the Canadian province of Alberta access and quality of stroke care were suboptimal, especially in remote areas. The government introduced the Alberta Provincial Stroke Strategy (APSS) in 2005, an integrated strategy to improve access to stroke care, quality and efficiency which utilizes telehealth. RESEARCH QUESTION: What is the process flow and the structure of the care pathways of the APSS? METHODOLOGY: Information for this article was obtained using documentation, archival APSS records, interviews with experts, direct observation and participant observation. RESULTS: The process flow is described. The APSS integrated evidence-based practice, multidisciplinary communication, and telestroke services. It includes regular quality evaluation and improvement. CONCLUSION: Access, efficiency and quality of care improved since the start of the APSS across many domains, through improvement of expertise and equipment in small hospitals, accessible consultation of stroke specialists using telestroke, enhancing preventive care, enhancing multidisciplinary collaboration, introducing uniform best practice protocols and bypass-protocols for the emergency medical services. DISCUSSION: The APSS overcame substantial obstacles to decrease discrepancies and to deliver integrated higher quality care. Telestroke has proven itself to be safe and feasible. The APSS works efficiently, which is in line to other projects worldwide, and is, based on limited results, cost effective. Further research on cost-effectiveness is 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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".