EVALUATING THE EFFECT OF A KNOWLEDGE TRANSLATION INTERVENTION ON IMPROVING THE CAPACITY OF STROKE TEAMS TO PROVIDE COMMUNICATIVE ACCESS FOR PERSONS WITH APHASIA
Bibliographic record
Abstract
Background Aphasia affects one-third of individuals with stroke resulting in their exclusion from everyday communication. Canadian stroke guidelines recommend clinicians be capable of enabling communicative access. Training to support sustainable capacity was identified as a gap in Toronto. The objective of this work was to evaluate the effect of a knowledge translation intervention on increasing capacity of stroke teams to use supportive conversation techniques with persons with aphasia (PWA). Methodology A mixed methods evaluation informed by the Knowledge-to-Action Process was conducted. Two speech-language pathologists (S-LPs) (1 from acute, 1 from inpatient rehabilitation), received three days of training in Supported Conversation for Adults with Aphasia (SCAu2122). Over a 6-month period, S-LPs assessed and addressed barriers to knowledge use, trained and mentored their local team with support from the Aphasia Institute. Stroke team members completed the Communicative Access Measures for Stroke tool to evaluate capacity to deliver SCAu2122 pre- and post-intervention. Each S-LP completed a qualitative report of their experiences. Results The questionnaire was completed by 42 individuals pre- and 30 post-intervention. The percentage of participants who agreed/strongly agreed they felt confident in communicating with PWA was 37% pre and 65% post. The percentage of participants who agreed/strongly agreed they felt effective communicating with PWA was 37% pre and 78% post. S-LPs described an increased use of SCAu2122 strategies and aphasia-friendly toolkits throughout the intervention. Conclusion The intervention may have contributed to increasing confidence and effectiveness of some stroke team members to provide SCAu2122 with patients post-stroke. Findings may be transferrable to other organizations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".