Better Health Durham: Community Engagement in a Cluster RCT of a Prevention Practitioner Intervention in Low-Income Neighbourhoods
Bibliographic record
Abstract
Background: The Building on Existing Tools to Improve Chronic Disease Prevention and Screening (BETTER) intervention has improved uptake of chronic disease prevention and screening activities in primary care. The BETTER intervention consists of 1:1 visits between prevention practitioners (PPs) and patients (40-65 years). It is unknown if an adapted BETTER could be effective in the community with public health nurses as PPs. Aim: The presentation objective is to describe community engagement strategies in a cluster RCT in low income neighborhoods with low cancer screening rates and low uptake of primary care. Methods: Principles of community-based participatory research were used to design the community engagement strategy in Durham region, Ontario. Key elements included close collaboration with public health partners to identify stakeholders and creating a community advisory committee (CAC) and a primary care engagement group to provide advice. Results: We identified 15 community stakeholder groups (∼47 subgroups) including service organizations, faith groups, and charitable organizations representing diverse constituents. Community outreach activities included in-person meetings and information displays at local events. The CAC is comprised of members of the public and representatives from primary care, social services, and community organizations. The CAC and primary care engagement groups have provided advice on trial recruitment strategies and on the design of the PP visit. Conclusion: The partnership between public health, primary care, and the study team has been crucial to connect with community stakeholders. Community engagement is essential in raising awareness about the study and will contribute to successful recruitment. Trial Registration: NCT03052959
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".