Using Community Engagement to Inform and Implement a Community-Randomized Controlled Trial in the Anishinaabek Cervical Cancer Screening Study
Bibliographic record
Abstract
Social, political, and economic factors are directly and indirectly associated with the quality and distribution of health resources across Canada. First Nations (FN) women in particular, endure a disproportionate burden of ill health in contrast to the mainstream population. The complex relationship of health, social, and historical determinants are inherent to increased cervical cancer in FN women. This can be traced back to the colonial oppression suffered by Canadian FN and the social inequalities they have since faced. Screening - the Papinacolaou (Pap) test - and early immunization have rendered cervical cancer almost entirely preventable but despite these options, FN women endure notably higher rates of diagnosis and mortality due to cervical cancer. The Anishinaabek Cervical Cancer Screening Study (ACCSS) is a participatory action research project investigating the factors underlying the cervical cancer burden in FN women. ACCSS is a collaboration with 11 FN communities in Northwest Ontario, Canada, and a multidisciplinary research team from across Canada with expertise in cancer biology, epidemiology, medical anthropology, public health, virology, women's health, and pathology. Interviews with healthcare providers and community members revealed that prior to any formal data collection education must be offered. Consequently, an educational component was integrated into the existing quantitative design of the study: a two-armed, community-randomized trial that compares the uptake of two different cervical screening modalities. In ACCSS, the Research Team integrates community engagement and the flexible nature of participatory research with the scientific rigor of a randomized controlled trial. ACCSS findings will inform culturally appropriate screening strategies, aiming to reduce the disproportionate burden of cervical disease in concert with priorities of the partner FN 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 | 0.107 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".