Supplementary Material for: The Association between Stroke, Integrated Stroke Systems, and the Employability and Productivity of Canadian Stroke Survivors
Bibliographic record
Abstract
Background: Integrated stroke systems have been associated with reduced disability in stroke survivors, but their impact on employability and productivity (hours worked/week and hourly wages) of stroke survivors is unclear. Methods: We used Canadian Community Health Surveys (2000–2014) to conduct a quasi-experimental study with the aim of determining the impact of stroke on employability and productivity, and whether the implementation of integrated stroke systems was associated with improvements in the employability and productivity of adult Canadian stroke survivors. Multivariable generalized Poisson regression models were used to determine the effect of stroke on employability; multivariable Heckman models were used to estimate the effect of stroke on productivity. A difference in differences analysis was used to determine the impact of stroke systems on the primary outcomes. Results: The study sample included 400,797 respondents, of whom 5,786 (1.4%) were stroke survivors. Compared to the general population, stroke survivors were less likely to be employed (adjusted incidence rate ratio 0.62, 95% CI 0.59–0.66) and earned an hourly wage that was 5.3% lower (95% CI 1.1–9.3) and worked an average of 1.9 fewer hours per week (95% CI 0.9–2.9). Over the study period, the employability of stroke survivors improved but not productivity. After accounting for temporal changes in overall employment, there was no association between the implementation of integrated stroke systems and either employability or productivity. Conclusion: Employability of stroke survivors improved over the study period but remained lower than the general population, with no differences in provinces with and without integrated stroke systems.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.676 | 0.082 |
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".