Exploring the Intersections of Storytelling and Visual Arts: Indigenous Peoples’ Experiences of Cancer
Bibliographic record
Abstract
Known gaps in health and social care, largely stemming from colonization, result in poorer outcomes for Indigenous peoples with cancer as compared to non-Indigenous peoples. Also, few researchers have focused on the strengths of Indigenous peoples in dealing with such challenges. Of note is a lack of research exploring Indigenous knowledge in this context and the ways in which such knowledge may be conveyed through stories and visual arts. With a view to exploring Indigenous cancer experiences, we completed a qualitative project with five communities in Canada. Data were collected via sharing sessions, photography and journaling, and individual interviews; all of these methods resulted in stories that were selected and shared by the participants themselves in their own words. The intersections of storytelling and visual arts were interpreted, resulting in three themes: (1) Singing, painting, and drawing stories connects to tradition; (2) Crafting stories connects the traditional and contemporary; and (3) Sharing stories connects participants to others. The results of this study have implications for culturally safe health care for Indigenous peoples with cancer, but also for the exploration of storytelling and the visual arts in health care more broadly.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".