Healthcare utilization after stroke in Canada- a population based study
Bibliographic record
Abstract
BACKGROUND: More people are surviving stroke but are living with functional limitations that pose increasing demands on their families and the healthcare system. The aim of this study was to determine the extent to which stroke survivors use healthcare services on a population level compared to people without a stroke. METHODS: This was a cross-sectional population-based survey that collected information related to health status, healthcare utilization and health determinants using the 2014 Canadian Community Health Survey. Healthcare utilization was assessed by a computer-assisted personal interview asking about visits to healthcare professionals in the last 12 months. Negative binomial regression was used to estimate the incidence rate ratios (IRR) and 95% confidence intervals (CI) for the number of health professional visits between stroke survivors and people without a stroke. The regression models were adjusted for demographics, as well as for mobility, mood/anxiety disorder and cardiometabolic comorbid conditions. RESULTS: The study sample included 35,759 respondents (948 stroke, 34,811 non-stroke) and equate to 12,396,641 (286,783 stroke; 12,109,858 non-stroke) when sampling weights were applied. Stroke survivors visited their family doctor the most, and stroke was significantly associated with more visits to most healthcare professionals [e.g., family doctor IRR 1.6 (CI 1.4-1.8); nurse IRR 3.0 (CI 1.8-4.8); physiotherapist IRR 1.8 (CI 1.1-1.9); psychologist IRR 4.0 (CI 1.1-5.7)] except the dental practitioner, which was less [IRR 0.7 (CI 0.6-0.9)]. Mood/anxiety condition, but not cardiometabolic comorbid condition increased the probability of visiting a family doctor or social worker/ counsellor among people with stroke. CONCLUSION: Stroke survivors visited healthcare professionals more often than people without stroke, and were approximately twice as likely to visit with those who manage problems that may arise after a stroke (e.g., family doctor, nurse, psychologist, physiotherapist). The effects of a stroke include mobility impairment and mood/ anxiety disorders. Therefore, adequate access to stroke-related healthcare services should be provided for stroke survivors, as this may improve functional outcome and reduce future healthcare costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".