A stakeholder engagement strategy for an ongoing research program in rural dementia care: Stakeholder and researcher perspectives
Bibliographic record
Abstract
Participatory research approaches have developed in response to the growing emphasis on translation of research evidence into practice. However, there are few published examples of stakeholder engagement strategies, and little guidance specific to larger ongoing research programs or those with a rural focus. This paper describes the evolution, structure, and processes of an annual Rural Dementia Summit launched in 2008 as an engagement strategy for the Rural Dementia Action Research (RaDAR) program and ongoing for more than 10 years; and reports findings from a parallel mixed-methods study that includes stakeholder and researcher perspectives on the Summit's value and impact. Twelve years of stakeholder evaluations were analyzed. Rating scale data were summarized with descriptive statistics; open-ended questions were analyzed using an inductive thematic analysis. A thematic analysis was also used to analyze interviews with RaDAR researchers. Rating scale data showed high stakeholder satisfaction with all aspects of the Summit. Five themes were identified in the qualitative data: hearing diverse perspectives, building connections, collaborating for change, developing research and practice capacity, and leaving recharged. Five themes were identified in the researcher data: impact on development as a researcher, understanding stakeholder needs, informing research design, deepening commitment to rural dementia research, and building a culture of engagement. These findings reflect the key principles and impacts of stakeholder engagement reported in the literature. Additional findings include the value stakeholders place on connecting with stakeholders from diverse backgrounds, how the Summit was revitalizing, and how it developed stakeholder capacity to support change in their communities. Findings indicate that the Summit has developed into a community of practice where people with a common interest come together to learn and collaborate to improve rural dementia care. The Summit's success and sustainability are linked to RaDAR's responsiveness to stakeholder needs, the trust that has been established, and the value that stakeholders and researchers find in their participation.
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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 | 0.138 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".