Physical Therapists' Perceptions of Factors Influencing the Acquisition of Motor Abilities of Children With Cerebral Palsy: Implications for Clinical Reasoning
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Evidence supporting factors predicting motor change for children with cerebral palsy is minimal. A consensus exercise using focus groups and survey methods was conducted to identify factors perceived to affect the acquisition of basic motor abilities among children with cerebral palsy from the time of diagnosis to 7 years of age. SUBJECTS: Fifty-seven physical therapists participated in one of 12 focus groups, and 60 physical therapists participated in a follow-up questionnaire survey via mail. METHODS: The nominal group technique was used to conduct the focus groups. RESULTS: Participants reached consensus about 12 factors in 4 constructs, which we called: (1) primary impairments (muscle tone/movement patterns, distribution of involvement, balance, and sensory impairment), (2) secondary impairments (range of motion/joint alignment, force production, health, and endurance), (3) personality characteristics (motivation), and (4) family factors (support to child, family expectations, and support to family). DISCUSSION AND CONCLUSION: The recognition of potential determinants of motor change could assist in the clinical reasoning that physical therapists use when planning interventions for children with cerebral palsy. Participants identified a set of variables, some of which are found in the literature, that can provide foundation knowledge for decision making and research on factors that bring about change in motor ability among children with cerebral palsy.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".