Pediatric Emergency Research Canada (<scp>PERC</scp>): Patient/Family‐informed Research Priorities for Pediatric Emergency Medicine
Bibliographic record
Abstract
BACKGROUND: A growing body of literature supports patient and public involvement in the design, prioritization, and dissemination of research and evidence-based medicine. The objectives of this project were to engage patients and families in developing a prioritized list of research topics for pediatric emergency medicine (PEM) and to compare results with prior research prioritization initiatives in the emergency department (ED) setting. METHODS: We utilized a systematic process to combine administrative data on frequency of patient presentations to the ED with multiple stakeholder input including an initial stakeholder survey followed by a modified Delphi consensus methodology consisting of two Web-based surveys and a face-to-face meeting. RESULTS: The prioritization process resulted in a ranked list of 15 research priorities. The top five priorities were mental health presentations, pain and sedation, practice tools, quality of care delivery, and resource utilization. Mental health, pain and sedation, clinical prediction rules, respiratory illnesses/wheeze, patient safety/medication error, and sepsis were identified as shared priorities with prior initiatives. Topics identified in our process that were not identified in prior work included resource utilization, ED communication, antibiotic stewardship, and patient/family adherence with recommendations. CONCLUSIONS: This work identifies key priorities for research in PEM. Comparing our results with prior initiatives in the ED setting identified shared research priorities and opportunities for collaboration among PEM research networks. This work in particular makes an important contribution to the existing literature by including the patient/family perspective missing from prior work.
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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 | 0.081 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".