Surveying Indigenous Cancer Support Needs Survey Design and Development
Bibliographic record
Abstract
Cancer survival rates are currently lower amongst Indigenous Peoples in Canada than non-Indigenous Peoples in Canada (1). Health professionals speculate that late cancer diagnosis and limited access to screening and support services are some of the main factors contributing to lower survival rate among Indigenous cancer patients (2). Fortunately, social supports have been found to improve cancer survival rates (3,4). Yet, there is little known about whether cancer support services meet the needs of Indigenous peoples. The purpose of this research was to create two survey tools that could evaluate the cancer support needs of Indigenous patients in Saskatchewan from both patient and health care provider perspectives. Surveys were created using existing cancer support surveys as reference, though none previously existed specific to Indigenous cancer patients. In addition, current literature surrounding Indigenous cancer supports was used to create the surveys along with informant input. Both surveys were created by matching survey content to themes to those found in an environmental scan and those in interviews from a study also evaluating cancer support needs for Indigenous patients. Surveys were validated using respondent validation and informant feedback. The result of this research was two survey tools; one to evaluate patient perspectives and another to evaluate health care provider or facilitator views on cancer support needs for Indigenous patients. The results of this study will benefit Indigenous cancer patients, their families, and their communities. The two surveys created in this study could help to inform health professionals and policy makers on the needs of Indigenous cancer supports in future research.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".