A Global Survey of Emergency Care Clinical Networks: Discussion and Implications for Canadian Learning Health Systems
Bibliographic record
Abstract
Clinical networks (CNs) can promote innovation and collaboration across providers and stakeholders.However, little is known about the structure and operations of CNs, particularly in emergency care.As Canada advances learning health systems (LHSs), foundational research is essential to enable future comparisons across CNs to identify those that contribute to positive system change.Drawing from the results of our international survey, we provide a description of 32 emergency care CNs worldwide, including their structure, operations and sustainability.Future research should consider the context of such networks, how they may contribute to an LHS and how they impact patient outcomes. RésuméLes réseaux cliniques (RC) peuvent favoriser l'innovation et la collaboration entre les fournisseurs et les intervenants.Cependant, on en sait peu sur la structure et le fonctionnement des RC, en particulier dans les soins d' urgence.Alors que le Canada s'intéresse aux systèmes de santé apprenants (SSA), la recherche fondamentale est essentielle pour permettre d'éventuelles comparaisons entre les RC afin de déterminer ceux qui contribuent au changement positif dans un système.À partir des résultats de notre enquête internationale, nous fournissons une description de 32 RC de soins d' urgence dans le monde, y compris leur structure, leurs activités et leur durabilité.Les recherches futures devraient tenir compte des contextes de ces réseaux, de la façon dont ils peuvent contribuer à un SSA et de leur incidence sur les résultats pour les patients.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.025 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".