Patterns of participation across a range of activities among <scp>C</scp>anadian children with neurodevelopmental disorders and disabilities
Bibliographic record
Abstract
AIM: Children with neurodevelopmental disorders and disabilities (NDD/D) may experience barriers or restrictions to participation in activities. We examined the extent to which this is a problem for children in particular NDD/D subgroups. METHOD: We analysed the 2006 Participation and Activity Limitation Survey children data set (5-14y) collected by Statistics Canada (n = 7072 and weighted n = 340 340), having identified the following NDD/D subgroups (weighted n = 77 470; 69.1% males and 30.9% females): gross or gross and fine motor (Motor(+) ), communication/cognition/learning (CCL), social interaction, neurosensory (vision or vision and hearing), and psychological. We used logistic regression to assess differences in participation in supervised and unsupervised physical activities, educational activities, and social/recreational activities. RESULTS: Participation in some school-based activities differed significantly among children in the NDD/D subgroups (p<0.01). Participation in supervised and unsupervised physical activity was lowest for the Motor(+) and social interaction subgroups, and highest for the neurosensory and CCL subgroups. Participation for the psychological subgroup was mostly in the intermediate range. In contrast, participation in educational activities was lowest for the social interaction and psychological subgroups, and higher for the other groups. INTERPRETATION: Given the importance of participation to child health and well-being, these differences in participation in various in-school activities highlight an area of need regarding policies/programmes to support subgroups of children with NDD/D.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".