“Breaking the Silence” to Improve Cancer Survivorship Care for First Nations Peoples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it