The impact of stroke on employment income: A cohort study using hospital and income tax data in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To document the impact of stroke on employment income among people employed at the time of stroke. DESIGN: Population-based cohort study. PARTICIPANTS: People hospitalized for stroke in Ontario, Canada (2010-2014) and people without stroke matched on demographic characteristics. MAIN MEASURES: Robust Poisson regression to estimate the effects of stroke on the probability of reporting employment income on tax returns over 3 years. Quantile regression difference-in-differences to estimate the changes in annual employment income attributable to stroke. RESULTS: Stroke survivors were increasingly less likely to report any employment income poststroke, incidence rate ratios (IRR) 0.87 at 1 year (95% confidence intervals [CI]; 0.85-0.88), 0.82 at 2 years (95% CI; 0.81-0.84) and 0.81 at 3 years (95% CI; 0.79-0.82). IRR for reporting at least 50% of prestroke income levels were 0.76 at 1 year (95% CI; 0.75-0.78), 0.75 at 2 years (95% CI; 0.73-0.77) and 0.73 at 3 years (95% CI; 0.71-0.75). IRR for reporting at least 90% of prestroke income levels were 0.72 at 1 year (95% CI; 0.70-0.74), 0.66 at 2 years (95% CI; 0.64-0.68) and again 0.66 at 3 years (95% CI; 0.64-0.68). Relative changes in annual employment income attributable to stroke varied from a decrease of 13.8% (95% CI; 8.7-18.9) at the 75th income percentile to a decrease of 43.1% (95% CI; 18.7-67.6) at the 25th income percentile. CONCLUSIONS: It is important for healthcare and service providers to recognize the impact of stroke on return to prestroke levels of employment income. Low-income stroke survivors experience a more drastic loss in employment income and may need additional social support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".