Studies on Children With Developmental Coordination Disorder in the Past 20 Years: A Bibliometric Analysis via CiteSpace
Bibliographic record
Abstract
Children with developmental coordination disorder (DCD) have been commonly observed and drawn an increasing amount of attention over the past decades. The aim of the present study is to evaluate the origin, current hotspots, and research trends on children with DCD using a bibliometric tool. After searching with “children” and “developmental coordination disorder” as the “topic” and “title” words, respectively, 635 original articles with 12,559 references were obtained from the electronic databases, Web of Science Core Collection (WoSCC). CiteSpace V.5.7.R2 was used to perform the analysis. The number of publications in this field was increasing over the past two decades. John Cairney from the Department of Family Medicine, McMaster University, Canada, was found to be the most productive researcher. Meanwhile, McMaster University and Canada were the most productive research institution and country, respectively. Reference and journal co-citation analyses revealed the top landmark articles and clusters in this field. Clumsiness was the most strength burst keyword. Moreover, task, meta-analysis, difficulty, adult, and impact will be the active research hotspots in future. These findings provide the trends and frontiers in the field of children with DCD, and valuable information for clinicians and scientists to identify new perspectives with potential collaborators and cooperative countries.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.128 | 0.142 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".