Engaging stakeholders in the process of embedding a type 2 diabetes prevention lifestyle program into a community setting: A collaborative approach
Bibliographic record
Abstract
Translation of efficacious health interventions into the community are often not applied in practice. The gap between research and practice is concerning for community members who can benefit from early access to effective health interventions. Knowledge translation activities and community partnerships are demonstrated methods to close the gap, yet there is a need for quality partnerships to ensure research findings are implemented into communities to ensure sustainability, rigour and quality programming through planning, preparation and time to foster the partnership. This presentation outlines the preparation process of translating an evidence-based program for improving health and exercise adherence in individuals with prediabetes into a community setting through a case example of a partnership between the YMCA of Okanagan and the Diabetes Prevention Research Group. The process involved three formalized translation events that enabled the group to work towards a long-term, successful partnership. These events included key stakeholders from research, community, and clinical settings. Stakeholder input was gathered to (a) identify roles and responsibilities of each partner in program sustainability and ongoing fidelity, (b) establish a training program and plan, and (c) develop a translational timeline. A video knowledge product was also created that documents the process of the partnership and program evolution to promote the program across the region. Insights from this process were imperative in informing the pilot implementation of the diabetes prevention program in the community, which is now ongoing. Moreover, this research provides insight into how community-research partnerships can collaboratively plan for and deliver programs within communities.Acknowledgments: Michael Smith Foundation for Health Research, Canadian Institute for Health Research
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 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 | 0.079 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".