Influence of Socioeconomic Status on Distance Traveled and Care After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Vital to maintaining an efficient delivery of services is an understanding of patient travel patterns during an acute ischemic stroke. Socioeconomic status may influence access to stroke care, including transportation and admission to different facility types. METHODS: We analyzed all acute ischemic stroke admissions between 2003 and 2007 through the Discharge Abstract Database, a national database containing patient-level sociodemographic, diagnostic, procedural, and administrative information across Canada. Socioeconomic status was defined in neighborhood quintiles according to Statistics Canada. Distances between patients and facilities were derived from postal codes. A principal diagnosis of ischemic stroke was identified using the International Classification of Diseases (versions 9 and 10). Analysis of variance and regression analyses were performed with adjustment for demographic characteristics. RESULTS: Admitted to acute care institutions were 243 410 patients with ischemic stroke. Mean patient age was 72.8 and 49.5% were male; 44.2% traveled beyond their closest center, amounting to an average 7.2 km additional distance traveled. Socioeconomic status quintile had minimal effect on travel patterns, with the lowest socioeconomic status accessing the closest center most frequently (odds ratio, 1.19; 95% confidence interval [CI], 1.13-1.16). Increased utilization of the closest hospital occurred with academic (odds ratio, 6.90; 95% CI, 6.69-7.11) or high-volume (odds ratio, 1.93; 95% CI, 1.88-1.98) facilities. Older patients (β=0.28; 95% CI, 0.27-0.28), expert destination facility (β=0.13; 95% CI, 0.12-0.14), and ambulance use increased travel beyond the closest center. CONCLUSIONS: Patients tend to choose care facilities based on hospital expertise; investment promoting improved regional facilities may be of greatest benefit to patients. Socioeconomic status has little bearing on travel patterns associated with stroke in Canada. These findings may assist in allocating funding to centers and improving patient care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".