Providing culturally safe cancer survivorship care with Indigenous communities: study protocol for an integrated knowledge translation study
Bibliographic record
Abstract
BACKGROUND: Cancer among Indigenous people is increasing faster than overall Canadian rates. Lack of survivorship support, including screening and follow-up for recurrences, contributes to poor health outcomes and low 5-year survival rates. Historical trauma from colonization and lack of culturally safe and responsive healthcare has negatively affected Indigenous peoples' access to survivorship supports. Nurses are typically the sole practitioners of health services in rural and remote Indigenous communities and can enhance the development, implementation, and delivery of culturally safe survivorship supports. However, the implementation of culturally safe healthcare in Indigenous communities is not well developed.This is the third study in a larger program of research with an overarching goal to improve healthcare delivery and outcomes with Indigenous people in Canada. In this study, we will field test nurses' implementation of cancer survivorship care with Indigenous people in Ontario, Canada. METHODS: The study is a descriptive participatory mixed methods research design involving a systematic review, field testing implementation of cancer survivorship supports in two communities, focus groups, and qualitative interviews. Outcomes include feasibility of implementation, acceptability of the strategies, and perceived impact on healing and psychosocial support. DISCUSSION: Results will advance knowledge about implementing culturally safe cancer survivorship supports with Indigenous people in Ontario. A toolkit will be developed to inform nursing practices, programs, and policies to improve cancer survivorship supports and strategies with Indigenous people. Findings will inform a large-scale implementation study to reduce healthcare disadvantages and disparities within Indigenous communities 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 | 0.075 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
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".