Why Do Clinical Practice Guidelines Get Stuck during Implementation and What Can Be Done: A Case Study in Pediatric Rehabilitation
Bibliographic record
Abstract
Aims: The purpose of this study was to obtain the perspectives of occupational and physical therapists working in pediatric rehabilitation about the factors that influence implementation of clinical practice guidelines (CPG) using the case of constraint induced movement therapy (CIMT). We aimed to identify factors that hinder implementation of CPGs and strategies for overcoming barriers when widespread implementation is stalled.Methods: Qualitative case study methodology was bounded within the parameters of CIMT implementation in pediatric rehabilitation in one jurisdiction. Twenty-one occupational and physical therapists participated in one of three focus groups. Data were analyzed using an inductive qualitative approach.Results: Participants viewed CPGs as useful, and emphasized the importance of consistency between guidelines and relevance to practice context. Therapists considered the “art and science” in clinical decision-making. Barriers and facilitators to CPG implementation were identified at the client, clinician, intervention location and systemic level. Potential solutions to help “unstick” guideline implementation were consistent with theories of collective knowledge exchange and mindlines.Conclusion: The presence of CPGs does not ensure evidence uptake; understanding of local barriers is required. This case study highlights the value of a collective knowledge exchange approach and attention to the social structures of knowledge development and evidence use.
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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 | 0.026 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| 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".