Factors Influencing Therapists' Interventions for Children with Learning Difficulties
Bibliographic record
Abstract
BACKGROUND: Occupational therapists use a range of intervention approaches with children with learning difficulties. PURPOSE: This study aimed to identify factors influencing therapists' clinical decisions when choosing and combining these interventions. METHODS: Seven occupational therapists participated. Data were collected through in-depth interviews, short questionnaires, and therapy observations and analysed thematically. FINDINGS: Therapists used a combination of child, therapist, and service-related factors to individually tailor a combination of intervention techniques drawn from different theoretical approaches. Therapists also used embedded practices with all children to engage and motivate them, promote task mastery, encourage application of strategies at home and school, and evolve intervention through ongoing evaluation. IMPLICATIONS: The child and family's home and school context and, to a lesser extent, the therapist's past experience and service context are primary factors considered when choosing interventions. Therapists continually move between theoretical and practical levels of thinking to best meet each child's 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 | 0.022 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".