The Challenge of Moving Evidence-Based Measures into Clinical Practice: Lessons in Knowledge Translation
Bibliographic record
Abstract
Once a measure has been developed and validated, it may take years for implementation into clinical practice. The purpose of this paper is to describe strategies used to increase knowledge translation and use of the Gross Motor Function Measure (GMFM) and the Gross Motor Function Classification System (GMFCS) in clinical practice in the Netherlands and reflect on the process. Knowledge translation strategies included peer-reviewed publications, workshops, and posting information on Web sites. The impact of several of these strategies was evaluated using questionnaires focusing on therapists' self-reported familiarity and use of the measures. Peer reviewed publications did not appear to impact clinical practice. Interactive workshops were more successful at increasing use, but a gap remained between knowledge and use; the transfer into clinical practice was not optimal. A stages of change model proposed by Grol and colleagues (2007) was a helpful framework for reflecting on the process, planning, and evaluation of strategies for facilitating change in clinical 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 | 0.456 | 0.651 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".