Managing Matajoosh: determinants of first Nations’ cancer care decisions
Bibliographic record
Abstract
BACKGROUND: Accessing cancer treatment requires First Nation peoples living in rural and remote communities to either commute to care, or to relocate to an urban centre for the length or part of the treatment. While Canadians living in rural and remote communities must often make difficult decisions following a cancer diagnosis, such decisions are further complicated by the unique policy and socio-historical contexts affecting many First Nation peoples in Canada. These contexts often intersect with negative healthcare experiences which can be related to jurisdictional confusion encountered when seeking care. Given the rising incidence of cancer within First Nation populations, there is a growing potential for negative health outcomes. METHODS: The analysis presented in this paper focuses on the experience of First Nation peoples' access to cancer care in the province of Manitoba. We analyzed policy documents and government websites; interviewed individuals who have experienced relocation (N = 5), family members (N = 8), healthcare providers and administrators (N = 15). RESULTS: Although the healthcare providers (social workers, physicians, nurses, patient navigators, and administrators) we interviewed wanted to assist patients and their families, the focus of care remained informed by patients' clinical reality, without recognition of the context which impacts and constrains access to cancer care services. Contrasting and converging narratives identify barriers to early diagnosis, poor coordination of care across jurisdictions and logistic complexities that result in fatigue and undermine adherence. Providers and decision-makers who were aware of this broader context were not empowered to address system's limitations. CONCLUSIONS: We argue that a whole system's approach is required in order to address these limitations.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".