Do Therapists’ Goals and Interventions for Children with Cerebral Palsy Reflect Principles in Contemporary Literature?
Bibliographic record
Abstract
In Brief Purpose: To explore therapists’ goal setting and intervention with children with cerebral palsy, and to examine their acceptance of children’s use of compensatory movement strategies. Methods: Interviews were conducted with 23 occupational therapists and 31 physical therapists. Goals and assumptions of relationships between intervention approaches and expected outcomes were coded using the International Classification of Functioning, Disability, and Health (ICF). Therapists’ acceptance of compensatory movement strategies was rated. Results: Thirty-three therapists identified goals representing the ICF activity component. Therapists working with younger children identified goals representing the ICF body function/structure component. Twenty-four therapists assumed that an intervention targeted at 1 ICF component would affect an outcome in a different component. Eleven therapists would not accept compensatory movement strategies. Conclusions: Most therapists’ goals are congruent with principles encouraging functional goals. The ICF matrix developed for this study may be useful for clinical evaluation and documentation of assumed relationships among interventions and outcomes. The authors' interviews of occupational and physical therapists indicate that most are establishing goals that are directed at the activity component of the ICF. Goals for younger children more often reflect therapists' concern for body structure and function.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".