Strengthening networks to improve knowledge translation in paediatric healthcare
Bibliographic record
Abstract
Background: Knowledge translation (KT), or the process of moving research into action, takes 10-20 years, resulting in sub-optimal healthcare for Canadians. Most KT strategies designed to shorten this gap neglect the social factors that facilitate research use. Social network analysis (SNA) methodology can be used to examine these factors within a network of individuals, including identifying influential people, and describing interaction patterns that can be targeted to improve KT efficiency. No such studies exist in paediatric healthcare organizations. Aims: 1) determine how SNA can augment KT research; 2) describe the national KT support context within paediatric healthcare and research organizations; and 3) confirm the influence of networks on KT, and identify network-driven KT support strategies. Methods: Study 1: Scoping review of SNA and theory applied to KT research. Study 2: Survey-based environmental scan of organizational KT supports in Canadian paediatric healthcare and research organizations. Study 3: Mixed-methods SNA descriptive case study of one healthcare-research organization dyad’s KT network using visual tools, and SNA survey and interview data from researchers, clinicians, leaders and KT support personnel to triangulate network influences on KT, and to identify network interventions to facilitate KT. Results: Study 1: SNA use is emerging in the KT field, primarily to examine information flow through cross-sectional survey research of physician-only networks, while analyzing few network properties. Diverse theoretical perspectives appear to be applicable for SNA research. Study 2: Organizational supports for KT typically targeted healthcare professionals, leaders and researchers, and included library services, KT support personnel, internal and external collaborations, forums and communication strategies, policies and protocols, consultation, specialized initiatives and funding. Study 3: Multiple network structures were perceived to influence KT. Reasons for network structure included individual attributes, relational considerations, and organizational context. Proposed network-driven KT support strategies included network development, communication, resources, personnel, visibly valuing KT, and evaluation. Conclusion: SNA can advance the science of KT by addressing the under-researched social determinants of evidence use, and by informing the design of network interventions. Participant engagement in applying a network perspective represented a novel application of SNA to KT research.
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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 | 0.072 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".