Embedding an enriched environment in an acute stroke unit increases activity in people with stroke: a controlled before–after pilot study
Bibliographic record
Abstract
OBJECTIVES: To determine whether an enriched environment embedded in an acute stroke unit could increase activity levels in acute stroke patients and reduce adverse events. DESIGN: Controlled before-after pilot study. SETTING: An acute stroke unit in a regional Australian hospital. PARTICIPANTS: Acute stroke patients admitted during (a) initial usual care control period, (b) an enriched environment period and (c) a sustainability period. INTERVENTION: Usual care participants received usual one-on-one allied health intervention and nursing care. The enriched environment participants were provided stimulating resources, communal areas for eating and socializing and daily group activities. Change management strategies were used to implement an enriched environment within existing staffing levels. MAIN MEASURES: Behavioural mapping was used to estimate patient activity levels across groups. Participants were observed every 10 minutes between 7.30 am and 7.30 pm within the first 10 days after stroke. Adverse and serious adverse events were recorded using a clinical registry. RESULTS: The enriched environment group ( n = 30, mean age 76.7 ± 12.1) spent a significantly higher proportion of their day engaged in 'any' activity (71% vs. 58%, P = 0.005) compared to the usual care group ( n = 30, mean age 76.0 ± 12.8). They were more active in physical (33% vs. 22%, P < 0.001), social (40% vs. 29%, P = 0.007) and cognitive domains (59% vs. 45%, P = 0.002) and changes were sustained six months post implementation. The enriched group experienced significantly fewer adverse events (0.4 ± 0.7 vs.1.3 ± 1.6, P = 0.001), with no differences found in serious adverse events (0.5 ± 1.6 vs.1.0 ± 2.0, P = 0.309). CONCLUSIONS: Embedding an enriched environment in an acute stroke unit increased activity in stroke patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".