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Enregistrement W4376279774 · doi:10.29173/cjen209

Exploring Mental health Barriers in Emergency Rooms (EMBER)

2023· article· en· W4376279774 sur OpenAlexaffvenueabout
Emily Hilton, Jacqueline Smith, Andrew C. H. Szeto, Stephanie Knaak, Eric C Chan, Rachel Grimminck, Jennifer Smith, Sarah R. Horn, Wafa Mustapha

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2023
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMental healthStigma (botany)Psychological interventionMental illnessNursingPublic healthIntervention (counseling)AddictionMedicinePsychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Background: Mental illness stigma is a complex public health issue that creates barriers for clients seeking services. For many clients, an ED visit may be their first point of contact with the health-care system for a mental illness/addiction crisis – but it often results in poor outcomes and negative experiences due to discriminatory and structural inequities. Calgary Health Foundation has funded a five-year multiphase study (EMBER) to explore stigma holistically through patients, families, physicians, psychiatrists, nurses, and protective services in FMC ED. The goal is to explore, address, understand, and evaluate interventions that mitigate stigma at both the individual and organizational levels. Methods: The EMBER research team is working collaboratively with AHS Policy Services team to examine mental health and addiction-related policies that may be connected to institutional stigma and practices that create barriers to access, help-seeking and the provision of mental health and addiction services. The ORBIT model is being used as a conceptual framework to support the cross-disciplinary approaches used by the research team to explore clinical and public health policy needs (phases 1 & 2); multiple intervention strategies (phase 3); targeted changes in health behaviors related to mental health stigma; and the potential of behavioral treatments to affect health outcomes (phases 4 & 5). Intervention implementation considerations include: (1) the perceived fit between proposed training and identified learning needs; (2) the suitability of intervention content for different learner groups; (3) intervention length; (4) format of delivery; (5) size of training groups; (6) mix of professionals within groups; (7) incentives for participation; (8) sustainability; (9) support for reinforcement of training over time; (10) anticipated implementation challenges and how to address them; and (11) expected or desired outcomes. Evaluation Methods: Addressing structural and resource inequities in the delivery of mental health/addiction care is a focal point of our study and an evidenced based pathway to ensure improved health outcomes for all Albertans. We are employing a mixed-method approach to capture quantitative and qualitative findings related to the experiences of patients/families, health care providers, and protective services as well as the policies that inform the delivery of care in ED settings. Evaluation throughout Phases 1/2 included thematic analysis of interview and focus group transcripts. In Phase 1, baseline surveys were used to collect demographics of participants and current levels of stigma amongst ED staff. Phase 2a, includes policy review through a human rights lens. In Phase 3, quantitative and qualitative surveys will be used pre- and post-intervention, and at follow up points (TBD). In Phases 4/5, the intervention data will be synthesized and used to inform recommendations for scale and spread. Results: Based on in-depth 60–90-minute focus groups or interviews with patients/families, health care providers, and protective services in phase one, the following results were captured using thematic analysis. Structural Stigma · Mental health rooms in the ED feel like “jail cells” · Staffing and other resource inequities for mental health care · Lack of training and role confusion Interpersonal Stigma · Patients/families: perceived lack of mental health training and resources, leading to unsatisfactory experiences, including in many cases, experiences of harm · Staff: inadequate mental illness training and occupational distress contribute to staff burnout and compassion fatigue Intrapersonal Stigma · Patients/families: lack of communication and dehumanizing interactions with staff contributing to feelings of isolation, shame, and hopelessness · Staff: vulnerability in disclosing personal mental health struggles Advice and Lessons Learned: Including a patient research partner in this study ensures that the voices of patients/families are heard, respected, and represented, and that a focus on patient-identified priorities and outcomes is maintained. Our PRP is an active and important member of our research team who acts as a liaison and role model during focus group discussions with patients and families. She assists with stigma reduction by using her lived experience and voice to educate others. More recently, we have identified professional silos within the healthcare system that have become the catalyst for promoting collaboration between EMBER researchers, AHS AMH clinical and operational leaders, and the Calgary Health Foundation (funder). Prioritizing the engagement of multiple stakeholders who have a direct interest in the process and outcomes of this study and how it is translated back into structural, policy and practice changes, is an important pathway to achieving sustained positive impact.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,021
score de la tête « metaresearch » (Gemma)0,032
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,994
Score d'incertitude au seuil0,110

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,032
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0020,004
Science ouverte0,0020,008
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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 Opus0,175
Tête enseignante GPT0,414
Écart entre enseignants0,239 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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

Citations0
Publié2023
Routes d'admission3
Résumé présentoui

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Même revueCanadian Journal of Emergency NursingMême sujetMental Health Treatment and AccessTravaux en français237 207