Solutions for Kids in Pain: A Knowledge Mobilization Network Built on a Foundation of Patient Partnership
Bibliographic record
Abstract
Background: Patient engagement is an approach that is expected or required to be part of a project, initiative or network by many research funding organizations. Solutions for Kids in Pain (SKIP) is a national knowledge mobilization network in Canada that was competitively funded and built on a foundation of engaging with patients (children and youth) and caregivers (parents) in its mission and vision. At the core of SKIP’s foundation was the PatientsIncludedTM charter, on which it grew and evolved its patient engagement efforts.Main Body: SKIP’s mission is to mobilize evidence-based solutions for children’s pain management. Unique to its funding requirements, SKIP was co-led by an academic institution (Dalhousie University) and a knowledge user partner (Children’s Healthcare Canada). SKIP is hosted at the university, where its central administration team is located, with six knowledge mobilization hubs based in cities across Canada. Patient engagement has been crucial to SKIP’s work with patient partners included in SKIP’s governance, management, committees, and knowledge mobilization activities. This paper shares and provides context for SKIP’s approach to patient partnership. How SKIP tailored its approach depending on the specific project context is demonstrated with three case studies. These case studies include SKIP’s Patient and Caregiver Advisory Committee which also developed resources that others may wish to use, the Youth in Pain Project which led to open calls for partnerships and unique approaches to listening and undertaking patient-informed projects, and Canada’s first national health standard for Pediatric Pain Management, which was co-developed with patient partners. Each unique case study demonstrates foundational principles to SKIP’s patient partnership such as offering compensation, creating a safe space, and others.Conclusion: Over its lifespan, SKIP committed to and wove patient partnership throughout all aspects of its network. We share the evolution of and insights gained from SKIP’s patient partnership activities, including resources for others to take and make their own. We encourage other research and knowledge mobilization networks to learn from this important patient partnership work and adopt and adapt what we share to their own contexts.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".