BEST PRACTICE IN SPEECH-LANGUAGE PATHOLOGY IN LANGUAGE AND COGNITIVE-COMMUNICATION POST STROKE
Bibliographic record
Abstract
Introduction: With 21% to 38% of individuals experiencing aphasia post stroke, language\nand communication impairments are major issues for clinicians working with stroke patients. To identify, assess, and treat language and communication impairments, speech-language pathologists (SLPs) use a wide variety of tools and interventions. However, the degree to which common screening, assessment, and treatment practices are supported by the evidence is unclear.\nMethod: This study (1) examined the actual screening, assessment, and treatment practices of 435 SLPs (as part of a cross-Canada survey); (2) identified best practice (via the consensus opinions of a clinician focus group); and (3) compared actual practice with best practice for aphasia and cognitive-communication impairment(s) post stroke. Results: Survey respondents (N=435) indicated 18 to 33 different screening and assessment tools, 27 to 33 additional methods or domains of screening and assessment, and 28 to 30 unique interventions as actual practice. Focus group participants (N=8) identified 20 to 22 different screening and assessment items, and 14 to 20 intervention items as best practice. The survey respondents provided vague descriptions of actual practice, whereas the focus group identified specific tools and interventions. This only allowed for general between-group comparisons. Conclusion: The actual screening, assessment, and treatment practices of SLPs for individuals with aphasia and cognitive-communication impairment(s) post stroke are diverse. The consensus opinions on best practice with this population identified the use of outcome measures with strong psychometric properties, as well as informal screening and\nassessment approaches. Both informal and evidence-supported interventions were also\nBest Practice Use\nBest Practice Use identified. The focus group noted best practice should be informed by the latest evidence,\nand be flexible to accommodate changing patient needs.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".