The impact of breast cancer among Canadian women: Disability and productivity
Bibliographic record
Abstract
Each year over 20,000 Canadian women are diagnosed with breast cancer. Many breast cancer survivors anticipate a considerable number of years of potential participation in the paid labour market, therefore, the link between breast cancer survivorship and productivity deserves serious consideration. The hypothesis guiding this study is that arm morbidities such as lymphedema, pain, and range of motion limitations are important explanatory variables in survivors' loss of productivity. The study draws from a larger longitudinal research project involving over 600 breast cancer survivors in four geographical locations across Canada. The study's regression results indicate that, after adjusting for fatigue, breast cancer stage, and geographical location, survivors with range of motion limitations and arm pain are more than two and half times as likely to lose some productivity capacity as compared to counterparts with no arm morbidity. The findings make a compelling argument for the necessity of adequate rehabilitation programs delivered at crucial times in breast cancer survivors' recovery. The study's unexpected finding that geographical location is a highly significant predictor of changes in productivity among breast cancer survivors is interpreted as a factor of the regulatory framework governing employment relationships in the four different jurisdictions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".