Experience of emergency department use among persons with a history of adverse childhood experiences
Notice bibliographique
Résumé
BACKGROUND: Adverse childhood experiences (ACEs) are associated with increased morbidity and mortality, lower levels of distress tolerance, and greater emotional dysregulation, as well as with increased healthcare utilization. All these factors may lead to an increased use of emergency department (ED) services. Understanding the experience of ED utilization among a group of ED users with high ACE scores, as well as their experiences as viewed through the lens of a trauma and violence informed care (TVIC) framework, could be important to their provision of care. METHODS: This is the qualitative portion of a larger mixed methods study. Twenty-five ED users with high ACE scores completed in depth interviews. Thematic analysis of the interview transcripts was undertaken and directed content analysis was used to examine the transcripts against a TVIC framework. RESULTS: The majority of participants experienced excellent care although challenges to this experience were faced by many in the areas of registration and triage. Some participants did identify negative experiences of care and stigma when presenting with mental health conditions and pain crises, as did participants who perceived that they were considered "different" (dressed differently, living in poverty, young parents, etc.). Participants were thoughtful about their reasons for seeking ED care including lack of timely access to their family doctor, perceived urgency of their condition, or needs that fell outside the scope of primary care. Participants' experiences mapped onto a TVIC framework such that their needs and experiences could be framed using a TVIC lens. CONCLUSIONS: While the ED care experience was excellent for most participants, even those with a trauma history, there existed a subset of vulnerable patients for whom the principles of TVIC were not met, and for whom implementation of trauma informed care might have a positive impact on the overall experience of care. Recommendations include training around TVIC for ED leadership, staff and physicians, improved access to semi-urgent primary care, ED patient care plans integrating TVIC principles, and improved support for triage nurses and registration personnel.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
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 tête enseignante, 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 ».