The Influence of Physical, Social, and Organizational Environments on Recreational Activities in Long-Term Care for Residents With Dementia: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: This scoping review explores how physical, social, and organizational factors influence recreational activity engagement for residents with dementia in long-term care settings. METHODS: A literature search in AgeLine, PsycINFO, CINAHL, Medline, and Web of Science identified studies on environmental factors affecting recreational activities for long-term care residents with dementia. A narrative approach was used to collate and summarize the findings from peer-reviewed English studies available until June 30, 2024. RESULTS: A total of 28 studies were reviewed, examining how physical, social, and organizational factors-such as homelike ambiance, staffing levels, and medicalized care culture-affect residents' engagement in recreational activities. The review also highlights the interrelationship among these factors. DISCUSSION AND IMPLICATIONS: The findings emphasize the importance of creating care environments that support activity participation. These insights can inform future assessments and the development of long-term care settings to improve activity experiences and outcomes for residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".