The characteristics of stroke units in Ontario: a pan-provincial survey
Bibliographic record
Abstract
Abstract Background Previous studies have demonstrated that organized, multidisciplinary care is the cornerstone of current strategies to reduce the death and disability caused by stroke. Identification of stroke units and an understanding of their composition and operation would provide insight for the further actions required to improve stroke care. The objective of this study was to identify and survey stroke units in Canadaâ s largest province, Ontario (population of 13 million) in order to describe availability, structure, staffing, processes of care, and type of population stroke units serve. Methods The Ontario Stroke Network (2011) list of stroke units and snowball sampling was used to identify all stroke units. During 2013 â 2014 an interviewer conducted telephone surveys with the stroke unit managers using closed and semi-open ended questions. Descriptive statistics were used to summarize survey responses. Results The survey identified 32 stroke units, and a respondent from every stroke unit (100% response rate) was interviewed. Twenty one were acute stroke units, 10 were integrated stroke units and one was classified as a rehabilitation stroke unit. Stroke units were available in all 14 Local Health Integration Networks except Central West. The estimated average number of stroke patients served per stroke unit was 604 with six-fold variation (242 to 1480) across the province. The typical population served in stroke units were patients with either ischemic or hemorrhagic stroke. Data consistently reported on the processes of stroke care, including the availability of multidisciplinary staff, specific diagnostic imaging, use of validated assessment tools, and the delivery of patient education. Details about the core components of stoke care were provided by 16 stroke units (50%). Conclusions This study demonstrates the heterogeneous structure of stroke units in Ontario and signaled potential disparity in access to stroke units. Many core components are in place, but half of the stroke units in Ontario do not meet all criteria. Areas for potential improvement include stroke care training for the multidisciplinary team, provision of individualized rehabilitation plans, and early discharge assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".