Knowledge translation initiatives at the Transitional Pain Service: insights from healthcare provider outreach and patient education
Bibliographic record
Abstract
Evidence-based treatment of chronic pain requires a multidisciplinary approach grounded in the biopsychosocial model. Implementing this approach within health systems relies on its acceptance by both healthcare providers and patients. While pioneering multidisciplinary pain clinics can serve as a model for implementation, a systematic effort is needed to share knowledge effectively and broadly. In the current paper we provide an overview of the knowledge translation initiatives undertaken at our Transitional Pain Service (TPS) at Toronto General Hospital, a state-of-the-art multidisciplinary pain program established in 2014 for patients at risk of developing chronic pain after surgery. The TPS team strives to enhance acceptance of this model of care among patients and providers, facilitate the establishment of similar clinics, and promote patient understanding of the integrated multidisciplinary pain care approach. Guided by the Knowledge to Action (KTA) framework, knowledge translation activities undertaken by our TPS team include clinician training, resources and outreach activities for providers, and patient education. Resource development was preceded by consultation and needs assessment among patients and providers and feedback from both groups was incorporated as part of the development process. The tailored resources were disseminated via the TPS clinic website and monitoring of online usage enables continuous evaluation of engagement. Barriers to engagement with the resources were examined through patient surveys and staff interviews. Based on these activities, we offer insights gained by our team throughout the knowledge translation process and provide recommendations for other clinical teams who wish to undertake similar initiatives.
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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 | 0.055 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".