Courage, compassion and connection, and the journey to healing: exploring cancer pre-diagnosis for Nunatsiavut Inuit
Bibliographic record
Abstract
Cancer is a chronic disease that has become increasingly prevalent in Indigenous populations within recent decades in Canada. Many risk factors contribute to the high rates of cancer for Indigenous peoples. Indigenous peoples have endured a history of colonialism, loss of culture, and dispossession of land. Indigenous peoples in the country present with later-stage cancers. This project was led by the Nunatsiavut Government in collaboration with the other Indigenous governments and organizations in Labrador: NunatuKavut Community Council, Mushuau Innu First Nation, and Sheshatshiu Innu First Nation, and explored the journey one must undergo to be diagnosed with cancer in Labrador, called the pre-diagnosis journey. Culturally safe approaches to data collection were used. We adopted a decolonizing approach with qualitative methods. This thesis will focus on findings from Nunatsiavut communities. Sharing circles and interviews were conducted with n= 32 participants. Thematic coding resulted in six themes: 1) Access and Supports; 2) Prolonged Investigation; 3) Travel; 4) Communication; 5) Fear and Anxiety; and 6) Being your own Health Advocate. Patients discussed challenges they encountered during their pre-diagnosis journey, and ways to improve their experience. There is a need for cultural-safety training for healthcare workers, a local cancer support group, accessible mental health services and educational materials about cancer. This thesis can be used to inform policy recommendations to enhance healthcare, and increase awareness of useful resources that can improve the pre-diagnosis 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".