Conceptualization and sources of costs from breast cancer: Findings from patient and caregiver focus groups
Bibliographic record
Abstract
Assessment of economic burden of breast cancer to patient and family has generally been overlooked in assessing the impact of this disease. We explored economic aspects from the perspective of women and their caregivers. Focus groups were conducted in 3 Quebec cities representing urban and semi-urban settings: 3 with 26 women first treated for non-metastatic breast cancer in the past 18 months, and 3 with 24 primary caregivers. We purposefully selected participants with different characteristics likely to affect the nature or extent of costs. Thematic content analysis was conducted on verbatim transcripts. Costs of breast cancer could be substantial, but were not the most worrisome aspect of the illness during treatments. Some costs were considered unavoidable, others depended on ability to pay. Costs occurred over a long period, with long term impact, and were borne by the whole family and not just the woman. Principal cost sources discussed were those associated with accessing health care, wage losses, reorganization of everyday life, and coping with the disease. This study provided deeper understanding of cost dynamics and the experience of costs among Canadian women with non-metastatic breast cancer, whose treatment and medical follow-up costs are borne through a system of universal, publicly funded health care.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".