Improving Quality of Life by Increasing Outings after Stroke: Study Protocol for the <i>Out-and-About</i> Trial
Bibliographic record
Abstract
RATIONALE: Almost one-third of Australians need help to travel outdoors after a stroke. Ambulation training and escorted outings are recommended as best practice in Australian clinical guidelines for stroke. Yet fewer than 20% of people with stroke receive enough of these sessions in their local community to change outcomes. AIMS: The Out-and-About trial aims to determine the efficacy and cost effectiveness of an implementation program to change team behavior and increase outings by people with stroke. DESIGN: A two-group cluster-randomized trial will be conducted using concealed allocation, blinded assessors, and intention-to-treat analysis. Twenty community teams and their stroke clients (n = 300) will be recruited. Teams will be randomized to receive either the Out-and-About program or written guidelines only. STUDY OUTCOMES: The primary outcome is the proportion of people with stroke receiving multiple escorted outings during therapy sessions, measured at baseline and 13 months postintervention. Secondary outcomes include number of outings and distance traveled, measured using a self-report diary at baseline and six months postbaseline, and a global positioning system after six months. Cost effectiveness will measure quality-adjusted life years and health service use, measured at baseline and six months postbaseline. DISCUSSION: A potential outcome of this study will be evidence for a costed, transferable implementation program. If successful, the program will have international relevance and transferability. Another potential outcome will be validation of a novel and objective method of measuring outdoor travel (global positioning system) to supplement self-report methods.
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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.102 | 0.026 |
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".