Widening the circle of care: An arts-based, participatory dialogue with stakeholders on cancer care for First Nations, Inuit, and Métis peoples in Ontario, Canada
Bibliographic record
Abstract
Cancer incidence is growing within First Nations, Inuit, and Métis (FNIM) communities, yet research and supportive care is slow to respond to their unique needs and experiences. The proposed project will engage important stakeholders involved in FNIM cancer care within Ontario, including health care professionals, health administrators, and FNIM community leaders. This study builds upon a national study on FNIM cancer survivors. Three objectives drive this research: 1) To identify strengths and needs within FNIM cancer care in Ontario from multiple perspectives; 2) To exchange knowledge of FNIM cancer experiences between stakeholders through arts-based methods, especially photography; 3) To work collaboratively with stakeholders to establish recommendations for improving FNIM cancer care. The project involves early consultations with stakeholders on the most pressing questions and issues in the area. Then, 20 participants (10 health care professionals, 5 health administrators, and 5 FNIM community leaders) will be recruited to use and discuss photos that capture experiences of FNIM cancer care. A report will be generated and dispensed to participants, bringing together various experiences, themes, perspectives, and recommendations for improving the state of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.049 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".