Improving outcomes for ill and injured children in emergency departments: protocol for a program in pediatric emergency medicine and knowledge translation science
Bibliographic record
Abstract
Approximately one-quarter of all Canadian children will seek emergency care in any given year, with the two most common medical problems affecting children in the emergency department (ED) being acute respiratory illness and injury. Treatment for some medical conditions in the ED remains controversial due to a lack of strong supporting evidence. The purpose of this paper is to describe a multi-centre team grant in pediatric emergency medicine (PEM) that has been recently funded by the Canadian Institutes of Health Research (CIHR). This program of research integrates clinical research (in the areas of acute respiratory illness and injury) and knowledge translation (KT). This initiative includes seven distinct projects that address the objective to generate new evidence for clinical care and KT in the pediatric ED. Five of the seven research projects in this team grant make significant contributions to knowledge development in KT science, and these contributions are the focus of this paper. The research designs employed in this program include: cross-sectional surveys, randomized controlled trials (RCTs), quasi-experimental designs with interrupted time-series analysis and staggered implementation strategies, and qualitative designs. This team grant provides unique opportunities for making important KT methodological developments, with a particular focus on developing a better theoretical understanding of the causal mechanisms and effect modifiers of different KT interventions.
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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 | 0.109 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".