Perspectives of Cancer Survivors with Low Income: A Content Analysis Exploring Concerns, Positive Experiences, and Suggestions for Improvement in Survivorship Care
Bibliographic record
Abstract
The number of cancer survivors in Canada has reached 1.5 million and is expected to grow. It is important to understand cancer survivors' perspectives about the challenges they face after treatment is completed. Many factors create barriers to accessing assistance, and limited income may be a significant one. This study is a secondary analysis of data from a publicly available databank (Cancer Survivor Transitions Study) regarding the experiences of Canadian cancer survivors. The goal was to explore major challenges, positive experiences, and suggestions for improvement in survivorship care for low-income Canadian cancer survivors one to three years following treatment. A total of 1708 survey respondents indicated a low annual household income (<$25,000 CD). A content analysis was performed utilizing written comments to open-ended questions. The major challenges respondents described focused on physical capacity limits and treatment side effects; positive experiences emphasized support and attentive care; and suggestions for improvements highlighted the need for better support, information about self-care and side effect management, and timely follow-up care. The relationships between household income and the management of survivors' physical, emotional, and practical concerns require consideration. The design of follow-up care plans, programs, services, and financial assessments of patients may prepare survivors for predictable issues and costs in their transition to survivorship.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".