Predictors of community participation from preschool to school age in children with cerebral palsy
Bibliographic record
Abstract
Abstract Aim To investigate participation frequency patterns and child and family predictors of community participation in young children with cerebral palsy (CP). Method We prospectively assessed participation frequency at preschool (Young Children's Participation and Environment Measure) and again at school age (Participation and Environment Measure‐Children and Youth). Linear regressions examined preschool predictors of community school‐age participation: preschool child age; sex; gross motor function (Gross Motor Function Classification System [GMFCS]); manual function (Manual Ability Classification System); pain; prosocial behaviour; conduct; family ethnicity; income; and residence type. Results Children with CP ( n = 155, 44% females, 64% classified in GMFCS level I or II), mean baseline age = 4 years 4 months (SD = 1 year 1 month) and at school age = 6 years 7 months (SD = 7 months) had a median community participation frequency at preschool age of 2.8 (interquartile range [IQR] = 1.3) and 2.8 (IQR = 1.6) at school age. Preschool community participation was 2.02 (confidence interval [CI] = −2.20 to −1.83) units lower than at home; at school age, it was 2.40 (CI = −2.59 to −2.22) units lower. Greater prosocial behaviour (child model: R 2 = 0.26, p = 0.001) predicted higher school age community participation. Interpretation In young children with CP, community participation was infrequent at preschool age (a few times in the last 4 months) and this persisted into school age. Higher preschool prosocial behaviour predicted community participation at school age. Enhanced awareness of infrequent community participation of preschool children with CP and supporting a child's social behaviours may help facilitate community participation.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".