Highlighting strengths and resources that increase ownership of cervical cancer screening for Indigenous communities in Northern British Columbia: Community‐driven approaches
Bibliographic record
Abstract
OBJECTIVE: To examine the unique and diverse strengths held by rural and remote Indigenous communities in northern British Columbia, including multi-generational support systems in health and wellness, profound connections to the land, and strong cultural foundations, and harness these strengths, allowing communities to engage in innovative and empowering health and wellness programs. METHODS: Building on these pre-existing and fundamental strengths, an alternative option to cervical cancer screening was introduced to nine Carrier Sekani health centers located in northern interior British Columbia in response to disparities in screening rates. Introduced in 2019, CervixCheck uses a self-collection approach that is private, safe, convenient, and offered at local community health centers by trained and supportive health staff. RESULTS: Using a strengths-based and community directed and descriptive approach, the process was outlined for a successful and ongoing health screening opportunity that is put into the hands of community members within Indigenous communities in the region of northern British Columbia. CONCLUSION: Through collaborative partnerships, in-person engagement sessions, and the utilization of pre-existing infrastructure and health and wellness teams, this project was successfully integrated into primary care centers using culturally safe and community-based approaches.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".