Financial burden from wage losses after early breast cancer: Extent and determinants among Canadian women
Bibliographic record
Abstract
9000 Background: Wage losses after breast cancer may result in considerable financial burden. More women now participate in the workforce and breast cancer is managed using multiple treatment modalities that could lead to long work absences. We evaluated the burden from wage losses and determinants among Canadian women in the first 12 months after newly diagnosed non-metastatic breast cancer. Methods: This prospective cohort study was conducted among 800 women from 8 hospitals (participation 83%) of whom 459 were working at diagnosis. For these latter women, information on potential determinants of wage losses, work absences, compensation received and perception of financial situation was collected by 3 telephone interviews over the year. Information on medical characteristics came from medical files. The main outcome was the relative loss, namely wages lost divided by annual wages the woman would have earned had she not been absent from work. ANOVA was used to identify determinants. Results: The median relative loss in the first year after diagnosis for the 403 women reporting an absence or reduced work hours was 19% or $5,502 (Can dollars). Multivariate analysis showed that the mean relative loss was 13% for women who reported that breast cancer was not at all costly compared to 22%, 33% and 38% among women who said that breast cancer was a bit, quite or very costly, respectively (ptrend<0.0001). A higher relative loss was significantly associated with a lower level of education (difference between lowest and highest levels = 8 %, ptrend=0.0016), living =50 km from the surgery hospital (diff = 6%, p=0.0697), lower social support (diff = 8%, p=0.0119), invasive disease (diff = 6%, p=0.0861), chemotherapy (diff = 17%, p<0.0001), self-employment (diff= 17%, p<0.0001), shorter tenure in the job (diff between lowest and highest levels = 12%, ptrend<0.0001) and part-time work (diff = 10%, p=0.0003). Conclusions: Financial effects of wage losses could add to the overall burden of breast cancer. Clinicians and policy makers should be sensitized further to the fact that financial burden may be important for working women having more aggressive treatment and precarious work situations. No significant financial relationships to disclose.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".