Evolution of a Community-Based Participatory Approach in a Rural and Remote Dementia Care Research Program
Bibliographic record
Abstract
BACKGROUND: Community-based participatory research (CBPR) approaches are valuable strategies for addressing complex health and social problems and powerful tools to support effective transformation of social and health policy to better meet the needs of diverse stakeholders. OBJECTIVES: Since 1997, our team has utilized CBPR approaches to improve health service delivery for persons with dementia and their caregivers in rural and remote settings. We describe the evolution of our approach, including benefits, challenges, and lessons learned over the last 15 years. METHODS: A multistage approach initiated an ongoing CBPR research program in rural dementia care and shaped its direction based on stakeholders' recommendation to prioritize both community and facility-based care. Strategies to develop and foster collaborative partnerships have included travel to rural and remote regions, province-wide community meetings, stakeholder workshops, creation of a Decision-Maker Advisory Council to provide ongoing direction to the overall program, development of diverse project-specific advisory groups, and a highly successful and much anticipated annual knowledge exchange and team-building event. LESSONS LEARNED: Partnering with stakeholders in the full research process has enhanced the research quality, relevance, application, and sustainability. These benefits have supported the team's evolution from a relatively traditional focus to an integrated approach guiding all aspects of our research. CONCLUSIONS: Developing and sustaining the full range of stakeholder and decision-maker partnerships is resource-and time-intensive, but our experience shows that community-based participatory strategies are highly suited to health services research that is designed to support sustainable service delivery improvements.
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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 | 0.186 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".