A multinational online survey of the goal setting practice of rehabilitation staff with stroke survivors with aphasia
Bibliographic record
Abstract
Purpose Goal setting is an essential rehabilitation activity. However, multidisciplinary rehabilitation staff goal-setting practice with stroke survivors with aphasia and associated training needs are not well understood.Methods We designed, piloted, and conducted a survey of stroke rehabilitation staff in the UK, Australia, Aotearoa New Zealand, Canada, Ireland. Analysis included descriptive statistics, chi-square and Fisher’s exact tests, and qualitative content analysis.Results We received 251 responses from 118 SLTs and 133 non-SLTs. Most reported setting goals with most or all people with aphasia (78%, 197/251); 57% (138/244) rarely or never provided an accessible copy of goals. All disciplines reported significantly less confidence setting goals with people with aphasia than without aphasia (p = 0.012, n = 119). Barriers to goal setting included the communication impairment (especially severe aphasia) and poor insight. Staff described feeling ill-equipped to support people with aphasia in goal setting; only 27% (67/251) had accessed training to do so.Conclusions Rehabilitation staff described involving stroke survivors with aphasia in goal setting but lacked confidence doing so and receive inadequate training and support. Training should target multidisciplinary staff confidence and communication support strategies and resources so that people with aphasia and families are supported as goal-setting partners.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".