“Breaking the Silence” to Improve Cancer Survivorship Care for First Nations Peoples
Bibliographic record
Abstract
There is a significant knowledge-to-action gap in cancer survivorship care for First Nations (FN) communities. To date, many approaches to survivorship have not been culturally responsive or community-based. This study is using an Indigenous knowledge translation (KT) approach to mobilize community-based knowledge about cancer survivorship into health-care programs. Our team includes health-care providers and cancer survivors from an FN community in Canada and an urban hospital that delivers Cancer Care Ontario’s Aboriginal Cancer Program. Together, we will study the knowledge-to-action process to inform future KT research with Indigenous peoples for improving health-care delivery and outcomes. The study will be conducted in settings where research relations and partnerships have been established through our parent study, The National Picture Project. The inclusion of community liaisons and the continued engagement of participants from our parent study will foster inclusiveness and far-reaching messaging. Knowledge about unique cancer survivorship needs co-created with FN people in the parent study will be mobilized to improve cancer follow-up care and to enhance quality of life. Findings will be used to plan a large-scale implementation study across Canada.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".