Cycles of silence: First Nations women overcoming social and historical barriers in supportive cancer care
Bibliographic record
Abstract
BACKGROUND: First Nations people with cancer in Canada confront several critical inequities in physical and psychosocial domains. First Nations women are at a particular disadvantage as they are disproportionately affected by social determinants of health, but how they navigate these challenges within their communities is poorly understood. OBJECTIVE: Our study explores survivorship experiences of First Nations women with cancer and their caregivers. Drawing from a larger data set on survivorship, we identify several major barriers to cancer communication and support in First Nations communities. METHODS: Our team conducted a participatory, arts-based study using several data collection methods (interviews, sharing sessions, photovoice, and other creative activities) with 43 participants (24 cancer survivors and 19 caregivers) from four First Nations communities in Canada. RESULTS: Two major themes have emerged out of our data analyses: (1) suffering without support leads to cycles of silence and (2) community-based supports can disrupt these cycles. We identified several social, historical, and institutional barriers to speaking about cancer and finding/providing support; however, communities met the challenge of silence through voluntary and unsolicited provision of support. CONCLUSIONS: Widespread silence around cancer reflects both the limited access First Nations people have to formal, supportive programs and services, as well as the creative ways they provide emotional, social, and financial support within their informal networks. Beyond the support of their communities, they also required institutional provision of care that is culturally safe, addressing the colonial impacts on cancer communication and the disproportionate burdens of disease in First Nations communities.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 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".