Supplementary Material for: Lost Productivity in Stroke Survivors: An Econometrics Analysis
Bibliographic record
Abstract
Background: Stroke leads to a substantial societal economic burden. Loss of productivity among stroke survivors is a significant contributor to the indirect costs associated with stroke. We aimed to characterize productivity and factors associated with employability in stroke survivors. Methods: We used the Canadian Community Health Survey 2011-2012 to identify stroke survivors and employment status. We used multivariable logistic models to determine the impact of stroke on employment and on factors associated with employability, and used Heckman models to estimate the effect of stroke on productivity (number of hours worked/week and hourly wages). Results: We included data from 91,633 respondents between 18 and 70 years and identified 923 (1%) stroke survivors. Stroke survivors were less likely to be employed (adjusted OR 0.39, 95% CI 0.33-0.46) and had hourly wages 17.5% (95% CI 7.7-23.7) lower compared to the general population, although there was no association between work hours and being a stroke survivor. We found that factors like older age, not being married, and having medical comorbidities were associated with lower odds of employment in stroke survivors in our sample. Conclusions: Stroke survivors are less likely to be employed and they earn a lower hourly wage than the general population. Interventions such as dedicated vocational rehabilitation and policies targeting return to work could be considered to address this lost productivity among 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.380 | 0.052 |
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".