Using Knowledge Exchange to Build and Sustain Community Support to Reduce Cancer Screening Inequities
Bibliographic record
Abstract
BACKGROUND: "Knowledge exchange" (KE) refers to the interaction between knowledge users and researchers toward a goal of mutual learning and collaborative problem solving. METHODS: Using a case study approach, this article describes how researchers leading a multiphase community engagement project, the Peel Cancer Screening Study (PCSS), used KE to engage a community advisory group (CAG) of knowledge users to build community support for interventions to reduce cancer screening inequities for South Asians in Peel Region, Ontario, Canada. RESULTS: As a result of KE activities (concept mapping, a CAG launch meeting, regular CAG meetings, workgroup meetings, a community report), there is currently a resident-targeted, community-level program being implemented and a provider-targeted intervention that is funded, with both ethnospecific and health service organizations involved. The process of KE received positive evaluations from advisory group members. CONCLUSIONS: The experiences of the PCSS illustrate the benefits of KE for researchers and community members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".