Impact of Income on Physical Concerns, Help Seeking, and Unmet Needs of Adult Cancer Survivors
Bibliographic record
Abstract
Purpose: Cancer treatment can have consequences for individuals which may have profound impact on daily living. Accessing assistance can be problematic. This study explores associations between income and concerns, help-seeking, and unmet needs related to physical changes following cancer treatment. Method: A national survey was conducted with cancer survivors about experiences with follow-up care one to three years after treatment. We report a trend analysis describing associations between income and cancer survivors' concerns, help-seeking, and unmet needs related to physical changes after treatment. Results: In total 5,283 cancer survivors between 18 and 64 years responded, of which 4,264 (80.7%) indicated annual household income. The majority of respondents were survivors of breast (34.4%), colorectal (15.0%), and prostate (14.0%) cancers. Over 90% wrote about experiencing physical changes following cancer treatment. Survivors with low annual household incomes of less than $25,000 (CAN) reported the highest levels of concern about multiple physical changes and were more likely to seek help to address them. Conclusions: Cancer survivors can experience various physical challenges and unmet needs following cancer treatment and difficulty obtaining relevant help across all income levels. Those with low income are more severely affected. Financial assessment and tailored follow-up are recommended.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".