Service providers’ perceptions of support needs for Indigenous cancer patients in Saskatchewan: a needs assessment
Bibliographic record
Abstract
BACKGROUND: In Saskatchewan, Canada, Indigenous cancer care services at the municipal, provincial, and federal levels are intended to improve quality care but can result in a complex, fragmented, and multi-jurisdictional health care system. A multi-phase needs assessment project was initiated to document Indigenous cancer care needs. Guided by Indigenous patient partners, clinicians, academics, and policy makers, the present study reflects a needs assessment of Indigenous cancer supports from the perspectives of cancer care service providers. METHODS: Qualitative data were collected through three focus groups with 20 service providers for cancer patients and their families at three Saskatchewan cities. Participants included chemotherapy and radiation nurses, social workers, a patient navigator, dieticians, and practicum students. A semi-structured interview guide was used to conduct the sessions to allow for freedom of responses. Data were recorded, transcribed verbatim, and analyzed using thematic analysis. RESULTS: Service providers' perspectives were categorized into five themes: 1) addressing travel-related issues, 2) logistical challenges, 3) improvements to Indigenous-specific health care supports, 4) cultural sensitivity in health care, and 5) consistency in care. Supports provided differed for the two Indigenous groups, First Nations and Métis. Service providers made recommendations regarding how needs could be met. They saw language translation providers and Elder supports as important. Recommendations for improving travel were for medical taxis to include breaks so that passengers may alleviate any uncomfortable side effects of their cancer treatment. Further, Indigenous-specific accommodations were recommended for those requiring medical travel. These recommendations aligned with supports that are available in four other Canadian provinces. CONCLUSIONS: These results identified gaps in supports and outlined recommendations to address barriers to cancer care from the perspectives of service providers. These recommendations may inform evidence-based health system interventions for Indigenous cancer patients and ultimately aim to improve cancer care services, quality of life, and health outcomes of Indigenous patients throughout their cancer journey.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".