Temporal Trends in the Unmet Health Care Needs of Canadian Stroke Survivors
Bibliographic record
Abstract
BACKGROUND: Stroke survivors have higher unmet health care needs than the general population. However, it is unclear whether such needs have changed over time, and whether these have been affected by the introduction of integrated systems of stroke care. METHODS: We used data from the Canadian Community Health Surveys between 2000 and 2014. We developed multivariable log-binomial generalized estimating equations to obtain adjusted risk ratios (aRRs) of unmet health care needs in stroke survivors compared to the general population, and over time. We conducted a difference in differences analysis to determine the association between the implementation of integrated systems of stroke care and unmet health care needs. RESULTS: Data from 350,084 respondents were included in the study; 8072 (2.3%) were stroke survivors. Compared to the general population, stroke survivors were more likely to report unmet health care needs (aRR 1.27; 95% CI, 1.22-1.32). The unmet health care needs reported by stroke survivors were lower after compared to before 2006 (15.8% vs. 31.9%, P < 0.001). After accounting for temporal trends, there was no association between the implementation of integrated systems of stroke care and change in unmet health care needs of stroke survivors. However, this requires cautious interpretation due to limitations in the data available for this study. CONCLUSIONS: Unmet health care needs of stroke survivors have reduced over time but remain higher than the general population. Future research should focus on identifying stroke- and policy-related factors to mitigate disparities in health care access for stroke survivors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".