Physical Therapists Are Key to Hip Surveillance for Children with Cerebral Palsy: Evaluating the Effectiveness of Knowledge Translation to Support Program Implementation
Bibliographic record
Abstract
AIMS: Physical and occupational therapists play a key role in the implementation of hip surveillance for children with cerebral palsy (CP) in British Columbia, Canada. We aimed to develop and assess a knowledge translation strategy to support the implementation of a provincial hip surveillance program. METHODS: Pediatric therapists were invited to participate in an anonymous survey assessing hip surveillance knowledge and learning needs. Based on these results, educational materials were developed. Two years later, one year following the launch of the hip surveillance program, the survey was repeated to assess learning, knowledge use, and barriers to enrollment. RESULTS: The initial survey was completed by 102 therapists; 74 therapists completed the second survey. Multifaceted educational strategies, including web-based learning, in-person education, email notifications, and print materials that targeted knowledge gaps were developed. Upon re-evaluation, knowledge increased on all questions. At follow-up, 45 therapists had enrolled a child, indicating knowledge use. Barriers to enrollment included lack of a CP diagnosis, parents or physicians not agreeing to enrollment, time requirements, and lack of space to complete the clinical exam. CONCLUSIONS: Targeted knowledge translation strategies were successful in meeting the educational requirements of a large group of therapists in a vast geographic area.
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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".