Validation of the Developmental Coordination Disorder Questionnaire for children aged 6 -10 years in the Czech Republic
Bibliographic record
Abstract
Problem: A specific developmental disorder of motor function affects up to one in 12 people. Neither the prevalence of DCD nor its diagnosis is completely uniform. According to some authors, even given the difficulty of existing tests and test batteries, a "gold standard" for diagnosing DCD is still being sought that would most effectively diagnose its prevalence. In Canada, a standardized screening method, the Developmental Coordination Disorder Questionnaire (DCDQ), has been developed as an auxiliary diagnostic tool to help detect DCD in children. It is an exploratory method that uses a questionnaire technique designed primarily for parents and teachers of children to diagnose the prevalence of this disorder. We believe that by using the DCDQ questionnaire, which according to Schoemaker is a "coarse screen" in identifying children with DCD, we will single out children with DCD and suspected DCD. Studies emphasize the importance of early identification of children with developmental coordination disorder (DCD), not only as a prevention of secondary academic, emotional, and social manifestations of the disorder. The questionnaire allows screening of children with motor difficulties. The DCDQ has not been validated in the Czech Republic and this is the aim of this study so that it can be used for the...
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".