Early intervention for children with/at risk of developmental coordination disorder: a scoping review
Bibliographic record
Abstract
AIM: To summarize current evidence for early identification and motor-based intervention for children aged 5 years and younger of age with/at risk of developmental coordination disorder (DCD). METHOD: Using scoping review methodology, we independently screened over 11 000 articles and selected those that met inclusion criteria. RESULTS: Of the 103 included articles, 78 articles were related to early identification and are summarized in a companion article. Twenty-two articles focused on early intervention, with an additional three articles covering both early identification and intervention. Most intervention studies were at a low level of evidence, but provide encouraging evidence that early intervention is beneficial for young children with/at risk of DCD. Direct intervention can be provided to whole classes, small groups, or individuals according to a tiers of service delivery model. Educating and building the capacity of parents and early childhood educators are also key elements of early intervention. INTERPRETATION: Evidence for early intervention for children with/at risk of DCD is emerging with promising results. Further studies are needed to determine best practice for early intervention and whether intervening early can prevent the negative developmental trajectory and secondary psychosocial consequences associated with DCD.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".