P.092 Successful implementation of a supported conversation program on an acute stroke unit
Bibliographic record
Abstract
Background: Aphasia is a life alerting deficit that affects up to 40% of people living with stroke. Barriers to communication ultimately impacts the care aphasic patients receive, as well as functional recovery. The Canadian Stroke Best Practice Recommendations suggest early and frequent language interventions to improve patients with aphasia quality of life, mood, and social outcomes. Methods: A supported conversation (SC) program (colloquially named The Aphasia Club) was implemented on the Acute Stroke Unit (ASU). The program included aphasia awareness and assessment training, as well as creation of an aphasia tool kit and discipline specific aphasia-friendly resources. Staff were encouraged to complete a 1-hour independent course on SC through the Aphasia Institute. Speech and language pathologists (SLP) offered an additional 30-minute in-person teaching session with interdisciplinary practice professionals. Following SLP assessment, personalized communication profiles were created for patients with aphasia to help staff understand the most useful strategies for communication. Results: More then 50 interprofessional staff members took SC training. Staff reported increased levels of knowledge and confidence when communicating with aphasic patients. Conclusions: A supported communication program was successfully implemented on an ASU. Planning appropriate communication interventions can assist interdisciplinary professionals in their ability to support patients through their stroke journey.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".