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Enregistrement W4376279919 · doi:10.29173/cjen214

Emergency department crowding: an overview of reviews describing measures causes, and harms

2023· article· en· W4376279919 sur OpenAlexaffvenue
Sabrina Pearce, Tyara Marchand, Erica Marr, Tara Shannon, Eddy Lang

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueEmergency and Acute Care Studies
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésCrowdingPsychological interventionSystematic reviewMedicineMEDLINEPsychologyPolitical scienceNursing

Résumé

récupéré en direct d'OpenAlex

Background: Crowding in Emergency Departments (EDs) has emerged as a global public health crisis. Current literature has identified causes and the potential harms of crowding in recent years. The way crowding is measured has also been the source of emerging literature and debate. We aimed to synthesize the current literature of the causes, harms, and measures of crowding in emergency departments around the world. Methods: This overview of reviews was guided by the PRIOR statement, and involved Pubmed, Medline, and Embase searches for eligible systematic reviews. A risk of bias and quality assessment, using the JBI tool, were performed for each included review, and the results were synthesized into a narrative overview. A total of 13 systematic reviews were identified, each targeting the measures, causes, and harms of crowding in global emergency departments. Results: The reviews addressed the current state of the literature regarding crowding in EDs and displayed that while an abundance of research is available, there is a need for further research to standardize measurements and make recommendations. Amongst the results is that the measures of crowding were heterogeneous, even in geographically proximate areas, and that temporal measures are being utilized more frequently. It was identified that many measures are associated with crowding, and the literature would benefit from standardization of these metrics to promote improvement efforts and the generalization of research conclusions. These standardized metrics may effectively be used to track crowding in geographically proximate centers, as well as to evaluate the impact of interventions and solutions on crowding in emergency departments. The major causes of crowding were grouped into patient, staff, and system-level factors; with the most important factor identified as outpatient boarding. A common theme in the causes of crowding was that issues were not universal; therefore, it is imperative to understand the issues relating to crowding in your center, the stage of treatment that it represents, and the actions that can be taken to reduce it. The harms of crowding include impacts to patients, healthcare staff, and healthcare service and spending. This harm may further exacerbate crowding, therein creating a cycle of poor healthcare delivery. Thus, it is imperative that systems target local solutions which can improve crowding in emergency care. Advice and Lessons Learned: This overview was intended to synthesize the current literature on crowding for relevant stakeholders, to assist with advocacy and solution-based decision making. The major conclusions from the overview were as follows: There is an abundance of current available research, especially on the measures of crowding, but a standard of metrics is required to standardize research results and accurately evaluate solutions to crowding. Crowding has a significant impact on patient care, employee satisfaction, and cost to the healthcare system, with worsening impacts on each factor as crowding worsens. The causes of crowding are heterogeneous, and solutions should be tailored to local healthcare systems. This is especially important considering the fact that a common theme was that many solutions were not tailored to the local causes of crowding. This project provides a broad overview on the topic of crowding and synthesizes the current evidence. The information contained within it provides a framework for concerted evidence informed efforts to reduce crowding in Emergency Departments.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,235
Score d'incertitude au seuil0,833

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,273
Tête enseignante GPT0,395
Écart entre enseignants0,122 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2023
Routes d'admission2
Résumé présentoui

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