Developmental Coordination Disorder in Alberta: A Journey into Knowledge Translation
Bibliographic record
Abstract
Aim: Developmental Coordination Disorder (DCD) is a motor disorder affecting coordination and performance in daily tasks. Studies regarding design and implementation of knowledge translation strategies effecting DCD practice changes in pediatric rehabilitation settings is limited and DCD remains under-recognized and under-diagnosed with an under-utilization of evidence-based practice. This knowledge-to-practice gap was identified in central Alberta, Canada when a lower caseload of clients with DCD and a lower rate of DCD referrals to a neurodevelopmental assessment clinic was observed, given its 5-6% prevalence in children. This paper describes and discusses the development and implementation of a replicable process of knowledge translation for integrating DCD evidence into frontline practice.Methods: Structures such as the Knowledge to Action framework were retrofitted and used to describe activities that occurred during the knowledge translation process. Main activities consisted of: information dissemination; education; addition of DCD to a neurodevelopmental assessment clinic; and development of a Community of Practice. Outcome evaluation methods included surveys, interviews, referral tracking, and database creation.Results: Knowledge translation strategy implementation resulted in increased knowledge among clinicians and community stakeholders, process standardization, increased referrals querying DCD, established knowledge brokers, and practice change.Conclusion: Pre-determined and systematic implementation strategy design is essential for embedding evidence into frontline practice.
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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 | 0.036 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".