Assessment practices of speech-language pathologists for cognitive communication disorders following traumatic brain injury in adults: An international survey
Bibliographic record
Abstract
PRIMARY OBJECTIVE: This study's objective was to examine the current assessment practices of SLPs working with adults with acquired cognitive communication impairments following a TBI. METHODS AND PROCEDURES: Two hundred and sixty-five SLPs from the UK, the US, Canada, Australia and New Zealand responded to the online survey stating the areas of communication frequently assessed and the assessment tools they use. MAIN OUTCOMES AND RESULTS: SLPs reported that they routinely assessed functional communication (78.8%), whereas domains such as discourse were routinely assessed by less than half of the group (44.3%). Clinicians used aphasia and cognitive communication/high level language tools and tools assessing functional performance, discourse, pragmatic skills or informal assessments were used by less than 10% of the group. The country and setting of service delivery influenced choice of assessment tools used in clinical practice. CONCLUSIONS: These findings have implications for training of SLPs in a more diverse range of assessment tools for this clinical group. The findings raise questions regarding the statistical validity and reliability of assessments currently used in clinical practice. It highlights the need for further research into how SLPs can be supported in translating current evidence about the use of assessment tools into clinical practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".