Measuring the Effects of Interventions on Participation in Children with Developmental Coordination Disorder (DCD)
Bibliographic record
Abstract
Abstract Purpose of Review The 2019 International clinical practice recommendations for developmental coordination disorder” recommended setting goals, and targeting interventions, at the activity and participation level. This review will explore how clinicians can make a positive move towards participation focused practice by measuring the impact of interventions at a participation level. Recent Findings Few studies have been published where the primary outcome measure is participation in activities which reflect child and family goals. The challenge of measuring the impact of participation focused interventions is setting goals and finding measures to capture both attendance and engagement from the child’s perspective. The Canadian Occupational Performance Measure (COPM) is frequently used to set goals and measure outcomes but does not consider levels of attendance and engagement. Summary Participation is a difficult construct to measure. There are tools available aimed at measuring participation, but they capture the parent/carer voice rather than the child’s. The recent publication of national survey studies on the impact of DCD provides valuable data in support of the development of participation focused services in multiple countries. Future research should continue to explore ways of capturing the child’s voice in measures of participation and continue to drive a better understanding of how participation focused practice can help mitigate some of secondary consequences reported in recent impact data.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".