Clinicians’ perspectives on motor learning strategy selection and application in occupational therapy and speech-language pathology interventions for children with acquired brain injury
Bibliographic record
Abstract
PURPOSE: Explore occupational therapists' (OTs) and speech language pathologists' (SLPs) process of selecting and applying motor learning strategies (MLS) in their interventions for children with acquired brain injury (ABI), and identify similarities and differences between OTs and SLPs in MLS selection and application. METHODS: This qualitative descriptive study involved individual semi-structured interviews with OTs and SLPs from the ABI program at Holland Bloorview Kids Rehabilitation Hospital (Toronto, Canada). Interviews were analyzed using thematic analysis. A modified constant comparison method permitted comparison within and between professions. RESULTS: Four OTs and three SLPs were interviewed. Four themes were developed: aligning MLS application with the child's cognitive ability, using MLS to promote success within a single session, adjusting MLS across treatment sessions, and promoting generalization and transfer of motor skills beyond the therapy session. MLS application was predominately based on child-specific factors with task-specific considerations. OTs and SLPs used similar clinical reasoning processes for selecting and applying MLS. CONCLUSIONS: This study provides a greater understanding of OTs' and SLPs' clinical reasoning process when applying MLS in pediatric ABI interventions. The similarities in MLS selection and application between disciplines suggest that an interprofessional approach to MLS is suitable for pediatric ABI rehabilitation.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".