An international survey of assessment and treatment practice for discourse in paediatric Acquired Brain Injury
Bibliographic record
Abstract
Guidelines recommend routine discourse assessment and treatment in paediatric acquired brain injury (ABI) but provide little guidance for clinical practice. The degree to which this has influenced the nature of discourse assessment and treatment in clinical practice has not been examined in detail. Speech-language pathologists working in paediatric ABI (clients aged <18 years) in Australia, New Zealand, the UK, the USA, Canada, and the Asia Pacific region were invited to complete a survey of discourse assessment and intervention practices (n = 77). Clinicians from Australia and New Zealand comprised over half of a responses (53%). The largest proportion had over 10 years’ experience (60%), worked in the metropolitan area (58%), and with secondary school-age children (64%). Routine discourse assessment was undertaken by 80% of respondents, focussing on a limited range of genres. No preferred intervention approach was identified. One-quarter of clinicians routinely considered holistic factors during clinical decision-making. Limited normative data and treatment evidence, insufficient time and training were identified as clinical barriers. Assessment practices were consistent with guidelines, yet interventions were highly variable, reflecting limited evidence, client heterogeneity, time constraints, and limited training. A biopsychosocial approach to practice was evident, yet a focus on impairment level factors was prominent. Findings support the need for standardised discourse assessment and discourse intervention methods. Translation into practice guidelines would promote consistency and confidence in 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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".