INCLUDING OLDER PEOPLE IN RESEARCH TOWARD CHANGE
Bibliographic record
Abstract
Through our respective programs of research, we seek to improve long term residential care services, support meaningful activity for older people in community settings, and enhance the physical environment for older patients in acute care hospitals. What unites our work is a commitment to using research as a vehicle for effecting positive change in the health and social care systems. This pragmatist leaning has led us as nurses to consider older people not as the focus of our research, but as our partners in research. We discuss three ways that older people have been involved in our work: as informants; as advisors; and as co-researchers. We provide examples from various projects to illustrate the challenges and benefits of older people being included through these kinds of partnering roles, and where we see potential to further develop this approach to applied nursing research.
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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 | 0.298 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".