Bridging the Gap between Clinical Research and Knowledge Translation in Pediatric Emergency Medicine
Bibliographic record
Abstract
In 2006, a multidisciplinary group of researchers from across Canada submitted a successful application to the Canadian Institutes for Health Research for a Canadian Institutes for Health Research Team in Pediatric Emergency Medicine. The conceptual foundation for the proposal was to bring together two areas deemed critical for optimizing health outcomes: clinical research and knowledge translation (KT). The framework for the proposed work is an iterative figure-eight model that provides logical steps for research and a seamless flow between the development and evaluation of therapeutic interventions (clinical research) and the implementation and uptake of those interventions that prove to be effective (KT). Under the team grant, we will conduct seven distinct projects relating to the two most common medical problems affecting children in the emergency department: respiratory illness and injury. The projects span the research continuum, with some projects targeting problems for which there is little evidence, while other projects involve problems with a strong evidence base but require further work in the KT realm. In this article, we describe the history of the research team, the research framework, the individual research projects, and the structure of the team, including coordination and administration. We also highlight some of the many advantages of bringing this research program together under the umbrella of a team grant, including opportunities for cross-fertilization of ideas, collaboration among multiple disciplines and centers, training of students and junior researchers, and advancing a methodological research agenda.
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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 | 0.442 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.026 | 0.076 |
| Scholarly communication | 0.041 | 0.037 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".