A Call for Collaboration: Knowledge Dissemination to Improve the Emergency Care of Children
Notice bibliographique
Résumé
To the Editor: Most children seeking emergency care are evaluated and treated in general emergency departments (EDs) that are not pediatric focused. Data from the 2013 Pediatric Readiness Survey have shown that many general EDs are not prepared to treat pediatric patients and that critically ill patients treated in ED with low readiness scores have a higher mortality.1 General EDs and nonpediatric hospitals often lack the equipment and expertise to implement treatment plans according to the latest pediatric guidelines and procedures. Furthermore, they are often located in hospitals without pediatric inpatient capabilities, resulting in transfers and delayed care.2 The well-documented 17-year lag between bench to bedside directly impacts patient outcomes. The Pediatric Emergency Care Applied Research Network (PECARN) has been conducting impactful research in pediatric emergency medicine for almost 20 years.3 The information garnered from their research endeavors has been an available resource to practitioners and patient families. However, the network acknowledges that the future challenge is disseminating and implementing those research findings into emergency medicine practice.3 One major barrier to knowledge dissemination and implementation is the lack of communication and collaboration between researchers and clinicians. The findings of research conducted in academic medical centers rather than in community settings are less likely to be adopted by physicians in their daily practice, including emergency medicine providers.4 Researchers may not have considered practical issues such as varied practice settings and differing knowledge bases and experiences of the practitioners tasked with implementing new evidence-based care. Knowledge dissemination frameworks take into consideration the knowledge generated, who will utilize the knowledge, how will the knowledge be utilized, and how will it impact patient care. Translating Emergency Knowledge for Kids (TREKK), an initiative by the Pediatric Emergency Research Canada (PERC), has had success in pediatric emergency medicine knowledge dissemination. TREKK has been successful in creating collaboration among PERC research centers and general EDs across Canada to determine knowledge needs and create resources to optimize patient care.5 As the collection of high-impact pediatric emergency medicine evidence grows, research networks in the United States are uniquely situated to lead knowledge dissemination efforts. Formalizing a collaboration between dedicated clinicians, researchers, content experts, professional societies, and clinical leaders from varied practice settings is the most appropriate next step. This collaboration will lead to utilization of research findings for the emergency care of children, regardless of the clinical setting. Attending periodic meetings, involvement in telehealth and simulation exercises, creating newsletters for circulation, and developing tools or mobile apps are some of the potential products of this collaboration. Knowledge dissemination and implementation will improve access to timely, evidence-based guidelines and treatment decision tools to ensure the best outcomes during the emergency care of children.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,088 | 0,368 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,013 | 0,021 |
| Science ouverte | 0,007 | 0,014 |
| Intégrité de la recherche | 0,034 | 0,029 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».