What does it take to facilitate the integration of clinical practice guidelines for the management of low back pain into practice? Part 2: A strategic plan to activate dissemination
Bibliographic record
Abstract
Low back pain (LBP) is the leading cause of disability worldwide among all musculoskeletal disorders despite an intense focus in research efforts. Researchers and decision makers have produced multiple clinical practice guidelines for the rehabilitation of LBP, which contain specific recommendations for clinicians. Adherence to these recommendations may have several benefits, such as improving the quality of care for patients living with LBP, by ensuring that the best evidence-based care is being delivered. However, clinicians' adherence to recommendations from these guidelines is low and numerous implementation barriers and challenges, such as complexity of information and sheer volume of guidelines have been documented. In a previous paper, we performed a systematic review of the literature to identify high-quality clinical practice guidelines on the management of LBP, and developed a concise yet comprehensive infographic that summarizes the recommendations from these guidelines. Considering the wealth of scientific evidence, passive dissemination alone of this research knowledge is likely to have limitations to help clinicians implement these recommendations into routine practice. Thus, an active and engaging dissemination strategy, aimed at improving the implementation and integration of specific recommendations into practice is warranted. In this paper, we argue that a conceptual framework, such as the theoretical domains framework, could facilitate the implementation of these recommendations into clinical practice. Specifically, we present a systematic approach that could serve to guide the development of a theory-informed knowledge translation intervention as a means to overcome implementation challenges in rehabilitation of LBP.
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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 | 0.454 | 0.407 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".