The state of physical environments in Australian residential aged care facilities
Bibliographic record
Abstract
Objective. This paper examines the quality and safety of the physical environment in Australian residential aged care facilities (RACFs). Design. Cross-sectional study. One assessor completed environmental audits to identify areas of the physical environment that needed to be addressed to improve the wellbeing and safety of residents. Setting. Nine RACFs participating in a broader falls prevention project were audited. RACFs were located in Queensland, Tasmania or Victoria and were chosen by convenience to represent high level, low level, dementia and psychogeriatric care, regional and metropolitan facilities, small and large facilities and a culturally specific facility. Main outcome measure. An environmental audit tool was adapted from a tool designed to foster older person friendly hospital environments. The tool consisted of 147 items. Results. Across all sites 450 items (34%) required action. This ranged from 21 to 44% across sites. The audit domains most commonly requiring action included signage, visual perception and lighting, and outdoor areas. Conclusions. Although not representative of all residential facilities in Australia, this audit process has identified common environmental problems across a diverse mix of residential care facilities. Results highlight the need for further investigation into the quality of physical environments, and interventions to improve physical environments in Australian RACFs. What is known about the topic? Despite the importance of the physical environment on the health, wellbeing and safety of older people in residential aged care facilities, few studies have comprehensively evaluated the physical environment in facilities in Australia. What does this paper add? This paper provides findings from comprehensive audits of nine residential aged care facilities representing a broad range of facility settings in terms of location, level and type of care and target population. Findings indicate that each facility had at least 21% of items requiring action with an average of 34% of items requiring action across all facilities. What are the implications for practitioners? There is a need to undertake intermittent, thorough assessments of the physical environments in which residents live and, if applicable, implement strategies or modifications to improve the environment. Areas requiring particular consideration may be lighting, colour contrasts, signage and outdoor areas.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".