Use of participatory action research approach to develop a self-management resource for persons living with dementia
Bibliographic record
Abstract
This article describes the use of a participatory action research (PAR) approach to developing a self-management resource for persons living with dementia and care partners. Despite growing evidence that persons with dementia are able to contribute in meaningful ways to decision-making about their care and life preferences, few opportunities exist for them to participate in the design of resources and services meant for them. There is also a need to support the self-management of persons living with dementia with the provision of accurate, high quality, user-friendly information. The Living Well with Dementia resource was developed through a partnership with persons with dementia, family members, Alzheimer Society representatives, primary care providers, and researchers. The methods used in the development of this resource are outlined in six steps employed in this process, from establishment of a PAR team to final resource creation. Informed by a whole systems approach, the resource brings together essential components of self-management into a comprehensive system of care and support for living. It empowers users to be active participants in the application of new knowledge to their lives. Better self-management has important implications for access to health care and quality of life for persons with dementia and care partners.
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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 | 0.086 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".