“Somebody That can Meet you on Your Level:” Cancer Survivors’ Perspectives on the Role of Indigenous Patient Navigators in Cancer Care
Bibliographic record
Abstract
BACKGROUND: Cancer incidence has increased for First Nations and Métis Peoples in Canada over recent years. Despite a growing cancer burden, there remain challenges to accessing culturally appropriate and quality care in Saskatchewan. OBJECTIVE: The study aimed to explore, from cancer survivors' perspectives, the potential of Indigenous patient navigators to enhance the healthcare experiences of First Nations and Métis cancer survivors in Saskatchewan. METHODS: We conducted semi-structured interviews with 19 First Nations and Métis cancer survivors. Participants were interviewed by phone between May 2022 to March 2023. Thematic analysis was conducted to generate themes, categories, and codes reflecting participants' experiences with patient navigators. RESULTS: Participants reported several supports to help patients with cancer through their cancer journey, including family, community, traditional ways, and First Nations and Métis health support services. Challenges to accessing care included language and communication barriers, logistical challenges, cultural differences, financial challenges, and gaps in care. Indigenous patient navigators may play a critical role in overcoming barriers by providing communication, translation, coordination, education, advocacy, and guidance to Indigenous cancer survivors. In participants' view, the tasks of Indigenous patient navigators could vary, ranging from helping schedule appointments to advocating for the patient's treatment preferences. In addition, Indigenous patient navigators could be helpful bridging Western medicine with traditional medicine when supporting patients seeking cancer care. CONCLUSION: According to study participants, Indigenous patient navigators could be beneficial to Indigenous cancer survivors in Saskatchewan.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".