PEER EDUCATION AND SUPPORT FOR HEALTHY AGING – A COMMUNITY BASED PARTICIPATORY APPROACH
Bibliographic record
Abstract
The number of people aged 65 years and older in the population is on the rise. Older adults, even those with good health, are more likely to have one or more chronic condition(s) and complex physical and social needs. Community Based Participatory Research (CBPR) offers a means to address the needs of older adults and include their perspectives and experiences in the research process. In this paper, we will share the lessons learned by researchers in employing a community based participatory approach in a mixed methods quantitative dominant, stepped-wedge cluster randomized trial that aims to assess the impact of trained peer health coaches on healthy aging behaviours, health literacy and health care seeking in community dwelling seniors. The SHAPES trial engaged seniors’ activity centres from the onset in the planning and study design. Drawing upon partnership and feedback from community organizations, a one hour workshop and three facilitated discussion sessions in the areas of healthy brain, healthy heart and healthy bones were developed for delivery to the participants. Based on health coaches’ feedback, the modules were modified to include more resources, simplify language and provide clarification where needed. The effects on recruitment and retention, practical constraints, methodological issues and other benefits and challenges of participatory approach involving older adults in the trial are discussed. It is possible to employ community based participatory approaches in robust clinical trials, however, there are certain limitations and challenges of which researchers should be aware and take measures to overcome.
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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 | 0.079 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".