Speech-language pathologists’ treatment goals for preschool language disorders: An ICF analysis
Bibliographic record
Abstract
PURPOSE: The World Health Organization's International Classification of Functioning, Disability and Health (ICF) provides a comprehensive framework to conceptualise clinical services. This study explored how speech-language pathologists (SLPs) conceptualised therapy goals for preschoolers with language difficulties and disorders within the ICF framework. METHOD: An online survey was distributed to SLPs practising in a publicly funded Preschool Speech and Language program in Ontario, Canada. SLPs rated their familiarity with the ICF framework, and then reported all therapy goals for one child with language difficulty/disorder on their caseload. For each reported goal, SLPs indicated the ICF component(s) they felt the goal addressed. Researchers then independently categorised SLPs' reported goals into the ICF components. RESULT: Ninety-three SLPs completed the survey, and 81% reported they were at least "somewhat" familiar with the ICF framework. On average, SLPs reported three therapy goals per child, and felt the Activities and Participation components were most frequently targeted (73% and 72% of all reported goals, respectively). Researchers categorised SLPs' reported goals differently, and identified 57% of goals addressing the Activities component, and 21% the Participation component. CONCLUSION: There is a need to better understand how SLPs and researchers conceptualise the ICF framework, particularly the Participation component.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".