Incorporating Older Adult Voice Into Meaningful Research – ‘It's About Time’
Bibliographic record
Abstract
Abstract Incorporating the voice of older adults into all phases of research has the potential to make findings more relevant and impactful. Beyond the direct benefit, researchers have an ethical obligation to elicit the contributions of older adults into their work. Recently, organizations such as the Patient Centered Outcomes Research Institute in the United States, the Canadian Institutes of Health Research and the National Institute of Health Research in the United Kingdom have stepped up to accelerate the incorporation of public and patient voice into research, resulting in innovative engagement strategies for involving stakeholders, including older adults in research. However, those who are physically and mentally capable are more often included in research than those with multiple chronic conditions or living with disabilities. The ability to incorporate older adult voice into research is possible and has provided tangible benefits to researchers. Older adults have expertise based on their lived experiences that can provide invaluable insights on how to conduct research with real-world applications. Programmes such as the Bureau of Sages have worked to implement and disseminate best practices and guidelines for incorporating the voice of older adults into research. Principles for engaging older adults include flexibility, mutual engagement of the older adult and the researcher, time for rapport building and partner development and increased focus on accessibility. By working to understand these principles and overcome challenges to incorporating older adult voice into research, research will be more meaningful and relevant to the public, and will inherently include a focus on translation of research into practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".