COMMUNICATION PARTNER TRAINING OF NURSES AND REHABILITATION PROFESSIONALS TO IMPROVE COMMUNICATIVE ACCESS OF PEOPLE WITH APHASIA IN AN ACUTE CARE HOSPITAL
Bibliographic record
Abstract
Communication difficulties associated with aphasia may lead to significant barriers preventing people with aphasia (PWA) from expressing their needs and fully participating in decision-making regarding medical care and rehabilitation. The knowledge and skills needed for healthcare workers to engage successfully in conversation with PWA can be learned through communication partner training (CPT). The goal of this study conducted at the McGill University Health Center was to provide further evidence of the benefits of CPT in acute care by determining the effect of training in Supported Conversation for Adults with Aphasia (SCATM) on healthcare workersu2019 attitudes and confidence in communicating with PWA. SCATM training was carried out with a group of nurses, physicians and rehabilitation staff. The participants completed a modified version of the Knowledge of Aphasia Scale before and after training to measure the impact it had on their perceptions of their interactions with PWA. Post training, the participants reported a higher level of knowledge about aphasia and that they used additional facilitating strategies when interacting with PWA. The average number of communication goals increased and they also reported improvement in their confidence level for conversing with PWA. SCATM training was beneficial to this group of acute care healthcare workers in increasing their perceived level of knowledge of aphasia, their confidence level for communicating with PWA and in the number of strategies they reported using to facilitate communication.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".