A Systematic Review of the Effectiveness of Knowledge Translation Interventions for Chronic Noncancer Pain Management
Bibliographic record
Abstract
BACKGROUND: Reliable evidence detailing effective treatments and management practices for chronic noncancer pain exists. However, little is known about which knowledge translation (KT) interventions lead to the uptake of this evidence in practice. OBJECTIVES: To conduct a systematic review of the effectiveness of KT interventions for chronic noncancer pain management. METHODS: Comprehensive searches of electronic databases, the gray literature and manual searches of journals were undertaken. Randomized controlled trials, controlled clinical trials and controlled before-and-after studies of KT interventions were included. Data regarding interventions and primary outcomes were categorized using a standard taxonomy; a risk-of-bias approach was adopted for study quality. A narrative synthesis of study results was conducted. RESULTS: More than 8500 titles and abstracts were screened, with 230 full-text articles reviewed for eligibility. Nineteen studies were included, of which only a small proportion were judged to be at low risk of bias. Interactive KT education for health care providers has a positive effect on patients' function, but its benefits for other health provider- and patient-related outcomes are inconsistent. Interactive education for patients leads to improvements in knowledge and function. Little research evidence supports the effectiveness of structural changes in health systems and quality improvement processes or coordination of care. CONCLUSIONS: KT interventions incorporating interactive education in chronic noncancer pain led to positive effects on patients' function and knowledge about pain. Future studies should provide implementation details and use consistent theoretical frameworks to better estimate the effectiveness of such interventions.
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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 | 0.031 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.015 | 0.013 |
| 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) | 0.007 | 0.001 |
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".