NATURAL ENVIRONMENTS’ INFLUENCE ON RESPONSIVE BEHAVIOUR, WORK-RELATED STRESS, BURNOUT, AND TURNOVER INTENTION
Bibliographic record
Abstract
Abstract Natural environments in healthcare design are thought to promote health. However, its empirical research in long-term care (LTC) has been limited; the degree of rigor is mixed, focusing on older adults with little research on professional caregivers. This study examined the relationships between the natural environment and the mental health and well-being of older adults 65+ living with Alzheimer’s disease and related dementias (residents) and of their professional caregivers in LTC homes using a rigorously developed survey to standardize an approach to the evaluation of natural environments for these population groups. A cross-sectional, multiphase mixed method study was conducted that included a scoping review (n=137), a Delphi panel (n=24 experts), cognitive debriefing interviews (n=33), psychometric evaluations (n=83 LTC home areas), and multilevel modeling and regression analyses (n=1599 residents; n=563 professional caregivers). A conceptual framework on the natural environment in healthcare was developed, which informed the development of the Natural Environment Survey for Long-Term Care. This survey evaluates 1) the design of natural environments, 2) residents’ usage of and exposure to natural environments, and 3) professional caregivers’ usage of, exposure to, and perception of natural environments. Unlocked doors to outdoor spaces, outdoor activities, and indoor activities were significantly associated with residents’ responsive behavior in secure home areas (i.e., locked access and egress). Usage of and exposure to the natural environment during breaks and leisure were associated with their professional caregivers’ work-related stress, burnout, and turnover intention in secure and non-secure home areas. These findings have potential utility for decision-makers transforming LTC.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".