Green Care Farms as an Approach to Support People Living with Dementia: An Exploratory Study of Stakeholder Perspectives
Bibliographic record
Abstract
How to best support people living with dementia and their care partners living in the community to maximize their quality of life and quality of living through appropriate and effective non-pharmaceutical approaches remains a focus of dementia societies and organizations worldwide. This paper examines the views of a range of stakeholders about the potential of green care farms in Canada, a country new to the concept of the green care farm approach to dementia support and care. Data were collected in Southern Ontario, Canada, between June and August 2022 via an online questionnaire (n = 12) and 1-1 interviews (n = 6). Questionnaire data were analyzed using descriptive statistics, specifically counts and frequencies. All interviews were audio-recorded and fully transcribed verbatim and analyzed thematically. We report thematic findings relating to the understanding of care farms for people living with dementia, perceived benefits of care farming, perceived enablers and barriers to implementing such an approach, and the hopes, motivations, and expectations of different stakeholders. The potential of green care farming for people living with dementia and their care partners in the Canadian context was evident. There are implications for care policy and practice relating to the promotion of (social) health and wellbeing for people living with dementia.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".