Innovating dementia care; implementing characteristics of green care farms in other long-term care settings
Bibliographic record
Abstract
ABSTRACTBackground:People with dementia at green care farms (GCFs) are physically more active, have more social interactions, are involved in a larger variety of activities, and come outdoors more often than those in other long-term dementia care settings. These aspects may positively affect health and well-being. This study explored which and how characteristics of GCFs could be implemented in other long-term dementia care settings, taking into account possible facilitators and barriers. METHODS: Semi-structured interviews were conducted with 23 professionals from GCFs, independent small-scale long-term care facilities, and larger scale long-term care facilities in the Netherlands. The framework method was used to analyze the data. RESULTS: Several characteristics of GCFs (e.g. homelike aspects, domestic activities, and access to outdoor environments) have already been applied in other types of long-term dementia care settings. However, how and the extent to which these characteristics are being applied differ between GCFs and other types of long-term dementia care settings. Facilitators and barriers for the implementation of characteristics of GCFs were related to the physical environment in which the care facility is situated (e.g. the degree of urbanization), characteristics and competences of staff members (e.g. flexibility, creativity), characteristics and competences of managers (e.g. leadership, vision), and the political context (e.g. application of risk and safety protocols). CONCLUSION: Several characteristics can be implemented in other dementia care settings. However, to realize innovation in dementia care it is important that not only the physical environment but also the social and organizational environments are supporting the process of change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".