Improving stroke care in Nova Scotia, Canada: a population-based project spanning 14 years
Bibliographic record
Abstract
Stroke is a complex disorder that challenges healthcare systems. An audit of in-hospital stroke care in the province of Nova Scotia, Canada, in 2004-2005 indicated that many aspects of care delivery fell short of national best practice recommendations. Stroke care in Nova Scotia was reorganised using a combination of interventions to facilitate systems change and quality improvement. The focus was mainly on implementing evidence-based stroke unit care, augmenting thrombolytic therapy and enhancing dysphagia assessment. Key were the development of a provincial network to facilitate ongoing collaboration and structured information exchange, the creation of the stroke coordinator and stroke physician champion roles, and the implementation of a registry to capture information about adults hospitalised because of stroke or transient ischaemic attack. To evaluate the interventions, a longitudinal analysis compared the audit results with registry data for 2012, 2015 and 2019. The proportion of patients receiving multidisciplinary stroke unit care rose from 22.4% in 2005 to 74.0% in 2019. The proportion of patients who received alteplase increased steadily from 3.2% to 18.5%, and the median delay between hospital arrival and alteplase administration decreased from 102 min to 56 min, without an increase in intracranial haemorrhage. Dysphagia screening increased from 41.4% to 77.4%. More patients were transferred from acute care to a dedicated in-patient rehabilitation unit, and fewer were discharged to residential or long-term care. These enhancements did not prolong length-of-stay in acute care. The network was a critical success factor; competing priorities in the healthcare system were the main challenge to implementing change. A multidimensional, multiyear, improvement intervention yielded substantial and sustained improvements in the process and structure of stroke care in Nova Scotia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".