Financial burden among survivors of childhood cancer: A report from the Childhood Cancer Survivor Study.
Bibliographic record
Abstract
233 Background: Cancer diagnosis and treatment can be associated with crippling financial burden, but whether this extends long-term into survivorship is unknown. We sought to examine survivors’ out-of-pocket (OOP) medical costs and their effects. Methods: From May 2011-April 2012, we surveyed a randomly selected sample of cancer survivors from the Childhood Cancer Survivor Study to assess survivors’ 1) financial distress, 2) monetary insecurity and 3) cost-motivated health behavior in the past year. We estimated the proportion of survivors with high OOP costs (≥10% of their annual household income). To determine associations between high OOP costs and the 3 outcomes of financial burden noted above, we used logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) for each of the outcomes, adjusting for sex, marital status, insurance, employment and income. Results: Of 1,101 mailed surveys, we received 698 (63% response) with a median age of 39 years (range 25-60) and 31 years from diagnosis (range 23-42). 9.3% (n=54) reported high OOP costs. Survivors with high OOP costs were more likely to report financial distress, monetary insecurity and cost-motivated health behavior. Conclusions: Adult survivors of childhood cancer may experience high OOP costs, resulting in significant financial burden. Our findings suggest that survivors’ OOP burdens not only influence their financial distress and monetary insecurities, but may also negatively impact their health behavior. [Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".