Financial Hardship and the Economic Effect of Childhood Cancer Survivorship
Bibliographic record
Abstract
In addition to the long-term physical and psychological sequelae of cancer therapy, adult survivors of childhood cancer are at an elevated risk for financial hardship. Financial hardship can have material, psychological, and behavioral effects, including high out-of-pocket medical costs, asset depletion and debt, limitations in or inability to work, job lock, elevated stress and worry, and a delaying or forgoing of medical care because of cost. Most financial hardship research has been conducted in survivors of adult cancers. The few studies focused on childhood cancer survivors have shown that these individuals are at elevated risk for having difficulties with affording needed health care and report high out-of-pocket medical expenses, difficulty with paying medical bills, or consideration of filing for bankruptcy. Childhood cancer survivors are more likely to be unable to work or to have missed work because of poor health. They are more likely to report difficulties with obtaining insurance coverage and rely more frequently on government-sponsored insurance. Globally, countries able to provide curative cancer therapies have witnessed a growing population of survivors, which places a burden on their health care systems because survivors are more likely to require hospitalization and experience a higher burden of chronic illness than the general population. Guidelines for surveillance for late effects are intended to reduce the burden of morbidity, but research is needed to determine whether such surveillance is cost effective. Of note, risk-based survivor care should include routine surveillance for financial hardship. Improved measures of financial hardship, enhanced data infrastructure, and research studies to identify survivors and families most vulnerable to financial hardship and adverse health outcomes will inform the development of targeted programs to serve as a safety net for those at greatest risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".