Wage losses among spouses of women with nonmetastatic breast cancer
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate the wage losses incurred by spouses of women with nonmetastatic breast cancer in the 6 months after the diagnosis. METHODS: A prospective cohort study of spouses of women diagnosed with nonmetastatic breast cancer who were recruited in 8 hospitals in the province of Quebec (Canada) was performed. Information for estimating wage losses was collected by telephone interviews conducted 1 and 6 months after the diagnosis. Log-binomial regressions were used to identify personal, medical, and employment characteristics associated with experiencing wage losses, and generalized linear models were used to identify characteristics associated with the proportion of usual wages lost. RESULTS: Overall, 829 women (86% participation) and 406 spouses (75% participation) consented to participate. Among the 279 employed spouses, 78.5% experienced work absences because of breast cancer. Spouses were compensated for 66.3% of their salary on average during their absence. The median wage loss was $0 (mean, $1820) (2003 Canadian dollars). Spouses were more likely to experience losses if they were self-employed or lived 50 km or farther from the hospital. Among spouses who experienced wage losses, those who were self-employed or whose partners had invasive breast cancer lost a higher proportion of wages. CONCLUSIONS: Although spouses took some time off work, for many, the resulting wage losses were modest because of compensation received. Still, the types of compensation used may hide other forms of burden for families facing breast cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".