Knowledge and Perception of caregivers on therapeutic gardens in Elderly care homes in Lagos Nigeria
Bibliographic record
Abstract
Abstract Gardening can be an activity that promotes overall health and quality of life, physical strength, fitness, flexibility, cognitive ability, and socialization [1]. Horticulture therapy employs plants and gardening activities in therapeutic and rehabilitation activities and could be utilized to improve the quality of life of the worldwide aging population [2]. In Nigeria families mainly provide support for their elderly ones [3], however, some families are gradually accepting the concept of care‐homes. Such care‐homes are in scarce supply and the available ones are operated by the private sector amidst limited resources. An appraisal of the availability of gardens in care‐homes showed that garden and gardening activities are mostly utilized in high‐income countries (HIC). However, the potential benefits of gardening activity are less well understood by related stakeholders in low‐ and middle‐income countries (LMICs) [4]. There is a dearth in the literature on the health impact of gardening in LMICs, thus the unavailability and lack of awareness of STH gardens in LMICs [4]. This study is an aspect of a social therapeutic horticulture project in two identified care homes in Lagos Nigeria. The study accesses the knowledge of caregivers and the management of STH gardens and the perceived need for such gardens in their respective homes. Utilizing online surveys and interviews for data collection. Currently, the study is ongoing, and ethical approval is being sourced. We aim to conclude the study before the scheduled 2023 AAIC conference. References 1. Wang, D., & MacMillan, T. (2013). The benefits of gardening for older adults: a systematic review of the literature. 2. Detweiler, M. B., Sharma, T., Detweiler, J. G., Murphy, P. F., Lane, S., Carman, J., & Kim, K. Y. (2012). What is the evidence to support the use of therapeutic gardens for the elderly? 3. Dokpesi, A. O. (2015). The future of elderly care in Nigeria: borrowing a leaf from a foreign land. 4. Ainamani, H. E., Bamwerinde, W. M., Rukundo, G. Z., Tumwesigire, S., Kalibwani, R. M., Bikaitwaho, E. M., & Tsai, A. C. (2021). Participation in gardening activity and its association with improved mental health among family caregivers of people with dementia in rural Uganda.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".