Exploring Differences in Stroke Treatment Between Urban and Rural Hospitals: A Thematic Analysis of Practices in Canada
Bibliographic record
Abstract
BACKGROUND: Treatment of acute ischemic stroke is highly time dependent, which relies heavily on each hospital's ability and capacity. Designated stroke centers have been established across Canada, but there is still a divide between urban and rural hospitals. This study aims to understand the similarities and differences in their stroke treatment process workflow, incorporation of best practices and data collection. METHODS: Interviews were conducted with clinicians in stroke centers across Canada to identify similarities and differences between provinces and hospital treatment capability. Semi-structured interviews were completed from September 15 to November 3, 2023, with clinicians and stroke coordinators using snowball and purposive sampling techniques. The interviews were analyzed using thematic analysis. RESULTS: Fourteen participants were interviewed with representatives from four primary stroke centers and three comprehensive stroke centers across five provinces. Five primary themes were identified: 1) management of resources, 2) standardization of tasks, 3) data collection, 4) tool integration into workflow and 5) teamwork and experience. Participants in primary centers described limited resources to follow the patient through the entire treatment process, reliance on pre-notification times to prospectively search necessary patient information, using software to aid in calculating National Institute of Health Stroke Scale and being more cautious toward treating thrombolytics. Both center types discussed challenges with complete and accurate data collection. CONCLUSIONS: The overall stroke treatment process and information required across primary and comprehensive centers are similar. However, differences occur in the process due to limitations in resources, pre-arrival notification time, completeness and accuracy of data collected and comfort in treating with thrombolytics.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".