Places & Spaces: A Critical Analysis of Cancer Disparities and Access to Cancer Care Among First Nations Peoples in Canada
Bibliographic record
Abstract
Despite advancements in research and medicine, health inequities and disparities among First Nations peoples (FN) in Canada are well documented and continue to grow. Once virtually unheard of, cancer now is a leading cause of death among FN. Many factors contribute to cancer disparities, but FN face unique challenges in accessing healthcare. In this critical review and analysis, we explore potential links between cancer disparities and poor access to cancer care among FN. Research suggests FN experience difficulty accessing cancer services in several ‘places’ of care, including screening, diagnosis, treatment, survivorship and palliative care. Furthermore, there are notable ‘spaces’ or gaps both within and between these ‘places’ of care likely contributing to cancer disparities among First Nations. Gaps in care result from jurisdictional ambiguities, geographical location, unsafe social spaces, and marginalization of FN ways of knowing, and can be linked to colonial and neocolonial policies and ideologies. By drawing attention to these broader structural influences on health, we aim to challenge discourses that attribute growing cancer disparities among FN in Canada solely to increases in ‘risk factors’.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".