‘We've fallen into the cracks’: Aboriginal women's experiences with breast cancer through photovoice
Bibliographic record
Abstract
Despite some recognition that Aboriginal women who have experienced breast cancer may have unique health needs, little research has documented the experiences of Aboriginal women from their perspective. Our main objective was to explore and to begin to make visible Aboriginal women's experiences with breast cancer using the qualitative research technique, photovoice. The research was based in Saskatchewan, Canada and participants were Aboriginal women who had completed breast cancer treatment. Although Aboriginal women cannot be viewed as a homogeneous group, participants indicated two areas of priority for health‐care: (i) Aboriginal identity and traditional beliefs, although expressed in diverse ways, are an important dimension of breast cancer experiences and have relevance for health‐care; and (ii) there is a need for multidimensional support which addresses larger issues of racism, power and socioeconomic inequality. We draw upon a critical and feminist conception of visuality to interrogate and disrupt the dominant visual terrain (both real and metaphorical) where Aboriginal women are either invisible or visible in disempowering ways. Aboriginal women who have experienced breast cancer must be made visible within health‐care in a way that recognizes their experiences situated within the structural context of marginalization through colonial oppression.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".