Diagnostic services for developmental coordination disorder: Gaps and opportunities identified by parents
Bibliographic record
Abstract
BACKGROUND: Affecting one in 20 children, Developmental Coordination Disorder (DCD) is a common neurodevelopmental disorder impacting a child's ability to learn motor skills. Despite its high prevalence, DCD is under-recognized and under-diagnosed, causing unnecessary frustration and stress for families who are seeking help for their child. This study aimed to understand how parents procure diagnostic services and their perspectives on needed supports and services to improve early identification and diagnosis of DCD. METHODS: Using a multi-pronged recruitment strategy, we circulated the impACT for DCD online questionnaire to parents of children (<18 years) in British Columbia with suspected or diagnosed DCD. Data were analysed descriptively using medians/interquartile ranges for continuous data and frequencies/percentages for categorical data. Open-ended questions were analysed using exploratory content analysis. RESULTS: A total of 237 respondent data were analysed. Parents identified poor awareness and understanding of health care professionals and educators regarding aetiology, symptomology, and impacts of DCD, affecting timely access to diagnostic services. Long waitlists were also a barrier that often led families with financial means to procure private diagnostic assessments. CONCLUSION: A standard of care is needed for streamlined diagnostic services, enabling early identification and early intervention. A publicly funded, family-centred, collaborative care approach is critical to assess, diagnose, and treat children with this disorder and to mitigate the secondary physical and mental health 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".