Effects of Cardiovascular Health Shocks on Spouses’ Work and Earnings
Bibliographic record
Abstract
BACKGROUND: Acute health shocks can reduce the ability to work and earn among working-age survivors. The full economic impact includes labor market effects on spouses/partners, but there is a knowledge gap in this area. OBJECTIVES: The objective of this study was to assess how 3 common health shocks, acute myocardial infarction, stroke, and cardiac arrest, influence work and earnings of spouses aged 35-61 years. RESEARCH DESIGN: This retrospective cohort study of case and control couples used population-based, linked Canadian income tax and hospitalization data from 2005 to 2013. SUBJECTS: Case couples comprised 1 partner aged 41-61 years who experienced a health shock in the index year and survived 3 years hence, and a working-age partner. Control couples were matched up to 5:1 on 12 characteristics, with neither experiencing the health shock of interest in the index year. MEASURES: Primary outcome was the change in spousal annual earnings between the year prior and 3 years after the event. Pre-to-post spousal income changes were categorized into 9 levels and compared between case spouses and control spouses by the Pearson χ test. RESULTS: There were 11,208 matched case couples for acute myocardial infarction, 622 for cardiac arrest, and 2288 for stroke. Overall, case and control spouses experienced similar distributional changes in preevent to postevent earning (all P≥0.27). Heterogeneity analysis indicated that spouses of more severe stroke sufferers ceased working at a higher rate than for control spouses. CONCLUSION: Beyond assessing average values, detailed analysis of changes in spousal earnings after common cardiovascular health shocks did not demonstrate effects attributable to those health shocks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".