Estimating the National Wage Loss from Cancer in Canada
Bibliographic record
Abstract
OBJECTIVES: Using primary and secondary data sources, we set out to estimate the Canadian wage loss from cancer for patients, caregivers, and parents from a patient and a societal perspective. METHODS: First, a multiple-database literature search was conducted to find Canadian-specific direct surveys of wage loss from cancer. Second, estimates for wage loss were generated from the nationally representative Canadian Community Health Survey (CCHS) Cycle 3.1. In addition, both estimates were standardized to derive a friction-period estimate and were extrapolated to produce national annual estimates. RESULTS: The literature search identified six direct surveys that included a total of 1632 patients with cancer. The CCHS Cycle 3.1 included 2287 patients with cancer. Overall, based on the direct surveys, newly diagnosed cancer patients reduced their labour participation in the friction period by 36% ($4,518), and caregivers lost 23% of their workable hours ($2,887). The CCHS estimated that annual household income was 26.5% lower ($4,978) for respondents with cancer as compared with the general population. For the year 2009, results from direct surveys indicated that new cancers in Canada generated a wage loss of $3.18 billion; the CCHS Cycle 3.1 estimate was $2.95 billion. CONCLUSIONS: Wage loss from cancer is a significant economic burden on patients, their families, and society in Canada, with direct surveys and the CCHS providing similar estimates.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".