Participation in physical play and leisure: developing a theory- and evidence-based intervention for children with motor impairments
Bibliographic record
Abstract
BACKGROUND: Children with motor impairments (e.g. difficulties with motor control, muscle tone or balance) experience significant difficulties in participating in physical play and leisure. Current interventions are often poorly defined, lack explicit hypotheses about why or how they might work, and have insufficient evidence about effectiveness. This project will identify (i) the 'key ingredients' of an effective intervention to increase participation in physical play and leisure in children with motor impairments; and (ii) how these ingredients can be combined in a feasible and acceptable intervention. METHODS/DESIGN: The project draws on the WHO International Classification of Functioning, Disability and Health and the UK Medical Research Council guidance for developing 'complex interventions'. There will be five steps: 1) identifying biomedical, personal and environmental factors proposed to predict children's participation in physical play and leisure; 2) developing an explicit model of the key predictors; 3) selecting intervention strategies to target the predictors, and specifying the pathways to change; 4) operationalising the strategies in a feasible and acceptable intervention; and 5) modelling the intervention processes and outcomes within single cases. DISCUSSION: The primary output from this project will be a detailed protocol for an intervention. The intervention, if subsequently found to be effective, will support children with motor difficulties to attain life-long well-being and participation in society. The project will also be an exemplar of methodology for a systematic development of non-drug interventions for children.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".