“I see beauty, I see art, I see design, I see love.” Findings from a resident-driven, co-designed gardening program in a long-term care facility
Bibliographic record
Abstract
BACKGROUND: Engagement with the natural environment is a meaningful activity for many people. People living in long-term care facilities can face barriers to going outdoors and engaging in nature-based activities. In response to needs expressed by our long-term care facility resident partners, we examined the feasibility and benefits of a co-designed hydroponic and raised-bed gardening program. METHODS: Our team of long-term care facility residents, staff and researchers co-designed and piloted a four-month hydroponic and raised-bed gardening program along with an activity and educational program, in 2019. Feedback was gathered from long-term care facility residents and staff through surveys (N = 23 at baseline; N = 23 at follow-up), through five focus groups (N = 19: n = 10 staff; n = 9 residents) and through photovoice (N = 5). A qualitative descriptive approach was applied to focus group transcripts to capture a rich account of participant experiences within the naturalistic context, and descriptive statistics were calculated. RESULTS: While most residents preferred to go outside (91%), few reported going outside every day (30%). Program participants expressed their joy about interacting with nature and watching plants grow. Analyses of focus group data generated the following themes: finding meaning; building connections with others through lifelong learning; impacts on mental health and well-being; opportunities to reminisce; reflection of self in gardening activities; benefits for staff; and enthusiasm for the program to continue. CONCLUSION: Active and passive engagement in gardening activities benefitted residents with diverse abilities. This fostered opportunity for discussions, connections and increased interactions with others, which can help reduce social isolation. Gardening programs should be considered a feasible and important option that can support socialization, health and well-being.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".