Understanding the unique and common perspectives of partners engaged in knowledge mobilization activities within pediatric pain management: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Knowledge mobilization (KM) is essential to close the longstanding evidence to practice gap in pediatric pain management. Engaging various partners (i.e., those with expertise in a given topic area) in KM is best practice; however, little is known about how different partners engage and collaborate on KM activities. This mixed-methods study aimed to understand what different KM partner groups (i.e., health professionals, researchers, and patient/caregiver partners) perceive as supporting KM activities within pediatric pain management. METHODS: This study used a convergent mixed-methods design. Ten partners from each of the three groups participated in interviews informed by the Consolidated Framework for Implementation Research, where they discussed what impacted KM activities within pediatric pain. Participants then rated and ranked select factors discussed in the interview. Transcripts were analyzed within each group using reflexive thematic analysis. Group-specific themes were then triangulated to identify convergence and divergence among groups. A matrix analysis was then conducted to generate meta-themes to describe overarching concepts. Quantitative data were analyzed using descriptive statistics. RESULTS: Unique themes were developed within each partner group and further analysis generated four meta-themes: (1) team dynamics; (2) role of leadership; (3) policy influence; (4) social influence. There was full agreement among groups on the meaning of team dynamics. While there was partial agreement on the role of leadership, groups differed on who they described as taking on leadership positions. There was also partial agreement on policy influence, where health professionals and researchers described different institutions as being responsible for providing funding support. Finally, there was partial agreement on social influence, where the role of networks was seen as serving distinct purposes to support KM. Quantitative analyses indicated that partner groups shared similar priorities (e.g., team relationships, communication quality) when it came to supporting KM in pediatric pain. CONCLUSIONS: While partners share many needs in common, there is also nuance in how they wish to be engaged in KM activities as well as the contexts in which they work. Strategies must be introduced to address these nuances to promote effective engagement in KM to increase the impact of evidence in pediatric 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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".