Barriers to cancer treatment and care for people experiencing structural vulnerability: a secondary analysis of ethnographic data
Bibliographic record
Abstract
BACKGROUND: A key pillar of Canada's healthcare system is universal access, yet significant barriers to cancer services remain for people impacted by structural vulnerability (e.g., poverty, homelessness, racism). For this reason, cancer is diagnosed at a later stage, resulting in worse patient outcomes, a reduced quality of life, and at a higher cost to the healthcare system. Those who face significant barriers to access are under-represented in cancer control services Consequently, these inequities result in people dying from cancers that are highly treatable and preventable, however; little is known about their treatment and care course. The aim of this study was to explore barriers to accessing cancer treatment among people experiencing structural vulnerability within a Canadian context. METHODS: We conducted a secondary analysis of ethnographic data informed by critical theoretical perspectives of equity and social justice. The original research draws from 30 months of repeated interviews (n = 147) and 300 h of observational fieldwork with people experiencing health and social inequities at the end-of-life, their support persons, and service providers. RESULTS: Our analysis identified four themes presenting as 'modifiable' barriers to inequitable access to cancer treatment: (1) housing as a key determinant for cancer treatment (2) impact of lower health literacy (3) addressing social care needs is a pre-requisite for treatment (4) intersecting and compounding barriers reinforce exclusion from cancer care. These inter-related themes point to how people impacted by health and social inequities are at times 'dropped' out of the cancer system and therefore unable to access cancer treatment. CONCLUSION: Findings make visible the contextual and structural factors contributing to inequitable access to cancer treatment within a publically funded healthcare system. Identifying people who experience structural vulnerability, and approaches to delivering cancer services that are explicitly equity-oriented are urgently needed.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".