Knowledge Translation Tools are Emerging to Move Neck Pain Research into Practice
Bibliographic record
Abstract
Development or synthesis of the best clinical research is in itself insufficient to change practice. Knowledge translation (KT) is an emerging field focused on moving knowledge into practice, which is a non-linear, dynamic process that involves knowledge synthesis, transfer, adoption, implementation, and sustained use. Successful implementation requires using KT strategies based on theory, evidence, and best practice, including tools and processes that engage knowledge developers and knowledge users. Tools can provide instrumental help in implementing evidence. A variety of theoretical frameworks underlie KT and provide guidance on how tools should be developed or implemented. A taxonomy that outlines different purposes for engaging in KT and target audiences can also be useful in developing or implementing tools. Theoretical frameworks that underlie KT typically take different perspectives on KT with differential focus on the characteristics of the knowledge, knowledge users, context/environment, or the cognitive and social processes that are involved in change. Knowledge users include consumers, clinicians, and policymakers. A variety of KT tools have supporting evidence, including: clinical practice guidelines, patient decision aids, and evidence summaries or toolkits. Exemplars are provided of two KT tools to implement best practice in management of neck pain-a clinician implementation guide (toolkit) and a patient decision aid. KT frameworks, taxonomies, clinical expertise, and evidence must be integrated to develop clinical tools that implement best evidence in the management of neck pain.
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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 | 0.324 | 0.527 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.031 | 0.056 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.038 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".