The effectiveness of interventions designed to increase the uptake of clinical practice guidelines and best practices among musculoskeletal professionals: a systematic review
Bibliographic record
Abstract
BACKGROUND: The objective of this systematic review was to summarize and evaluate evidence about the effectiveness of knowledge translation (KT) interventions to improve the uptake and application of clinical practice guidelines and best practices for a wide range of musculoskeletal (MSK) disorders and health care professionals. METHODS: A search for relevant randomized controlled trials (RCTs) published in English was conducted in MEDLINE (Ovid interface), EMBASE, CINAHL, and CENTRAL (Cochrane library). Two independent reviewers selected studies, assessed risk of bias, and extracted data. All MSK disorders were included except MSK injuries, fractures, trauma, or inflammatory disorders. RESULTS: A total of 7904 citations yielded 11 eligible RCTs. The targeted MSK disorders included: low back pain (n = 5), neck pain (n = 2), whiplash (1), spinal disorders (n = 1), and osteoarthritis of the hip and knee (n = 2). Studies primarily involved physiotherapists, chiropractors, and a mix of physiotherapists, chiropractors and osteopaths. Results were reported using effect sizes (Cohen's d). Interactive educational meetings were the most commonly used KT strategy. For professional outcomes, 3 studies using single-component interventions had a small effect (d ranges from 0.14 to 0.28) and 7 studies used multifaceted interventions (3 were effective (d ranges from 0.824 to 2.27). For patient outcomes, 4 studies were ineffective (d ranges from 0.06 to 0.31). The majority of the included RCTs had moderate-to-high risk of bias. About half of the studies used theory-based interventions, but the elements of the interventions and theoretical frameworks were often poorly described. Furthermore, there were no comparable outcome measures to evaluate the impact of the interventions on a similar scale. CONCLUSIONS: The findings suggested that multifaceted educational KT interventions appear to be effective for improving professional outcomes, although effects were inconsistent. The KT strategies were generally not effective on patient outcomes. In general, studies were of low quality, interventions were poorly described, and only half had theoretical underpinning. Researchers are encouraged to use validated professional and patient outcomes.
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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 | 0.029 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".