Bibliographic record
Abstract
Research about patients with dementia in the context of acute care has been traditionally designed and carried out by researchers with little or no involvement of people with dementia. Moving away from the traditional way of conducting research on people with dementia, this study involved people with dementia as experts of lived experiences to co-develop knowledge for change. The paper presents our shared experiences (a person with dementia and a researcher) gained from an action research, titled Co-creating Person-Centred Care in Acute Care. We highlight our successes and possibilities for making real impacts on hospital care for patients with dementia by using an appreciative inquiry approach. The project was informed by the core principles of appreciative inquiry. The research involved seven patients with dementia together with a team of 50 interdisciplinary staff to inquire and take actions for improving dementia care in a medical unit. This article draws attention to a range of ethical responsibilities and challenges, which go beyond the traditional principles in University Research Ethics. The strengths and challenges of conducting action research with people living with dementia are discussed. We conclude by offering our learnings and practical tips to encourage more collaboration between researchers and people with dementia in undertaking action research to make social change.
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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 | 0.084 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.013 |
| 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".