"My Cancer Is Worth Only Fifteen Weeks?" A Critical Analysis of the Lived Experiences of Financial Toxicity and Cancer in Canada
Bibliographic record
Abstract
BACKGROUND: Cancer patients experience financial hardship due to rising expenses related to cancer treatment and declining income levels associated with reduced employability. Employment Insurance Sick Benefits (EI-SB) is a social income support program which provides temporary income replacement to Canadians when they fall ill. Although EI-SB is designed to maintain continuity of income during an illness, little is known about the perspectives of cancer patients who receive EI-SB. This knowledge can inform the development of public policies which are responsive to the needs and priorities of cancer patients. METHODS: We conducted a theory-informed thematic analysis of data collected from twenty semi-structured interviews with participants who were receiving care in a cancer centre in Cape Breton, Nova Scotia and had received EI-SB. A coding framework was developed using Taplin and colleagues' intermediate outcomes of patient care across the cancer care continuum. Interpretation of findings was guided by the synergies of oppression theoretical lens. RESULTS: Three overarching themes describe the experiences of cancer patients receiving social income support: Economic exclusion, in which the structure of the labour market and social welfare system determine access to workplace benefits and continuity of reasonable income; financial toxicity, a vicious cycle of financial burden and increasing financial distress; and constrained choices, where cancer influences employability and lowered income influences the need to be employed. CONCLUSION: Cancer patients need income support programs that are tailored to match their healthcare priorities. In addition, policies which strengthen working conditions and facilitate a reintegration to work when possible will be important in addressing the structural drivers of income insecurity experienced by cancer patients.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.045 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".