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Enregistrement W3120073104 · doi:10.1186/s40900-020-00247-w

Engaging a person with lived experience of mental illness in a collaborative care model feasibility study

2021· article· en· W3120073104 sur OpenAlexafffund
Lenka Vojtila, Iqra Ashfaq, Augustina Ampofo, Danielle Dawson, Peter Selby

Notice bibliographique

RevueResearch Involvement and Engagement · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensPublic Health OntarioCentre for Addiction and Mental Health
Organismes subventionnairesMedical Psychiatry Alliance
Mots-clésMental healthNursingVariety (cybernetics)Health carePsychologyLived experienceMental illnessCollaborative CareMedicineMedical educationPsychiatryPsychotherapist

Résumé

récupéré en direct d'OpenAlex

Researchers have explored different types of treatment to help people with a mental illness with other problems they might be experiencing, such as their health condition and quality of life. Care models that involve many different health care providers working together to provide complete physical and mental health care are becoming popular. There has been a push from the research community to understand the value of including people with lived experience in such programs. While research suggests that people with lived experience may help a patient's treatment, there is little evidence on including them in a team based program. This paper describes how our research team included a person with lived experience of psychosis in both the research and care process. We list some guiding principles we used to work through some of the common challenges that are mentioned in research. Lastly, experiences from the research team, lessons learned, and a personal statement from the person with lived experience (AA) are provided to help future researchers and people with lived experience collaborate in research and healthcare. Background In our current healthcare system, people with a mental illness experience poorer physical health and early mortality in part due to the inconsistent collaboration between primary care and specialized mental health care. In efforts to bridge this gap, hospitals and primary care settings have begun to take an integrated approach to care by implementing collaborative care models to treat a variety of conditions in the past decade. The collaborative care model addresses common barriers to treatment, such as geographical distance and lack of individualized, evidence-based, measurement-based treatment. Person(s) with lived experience (PWLE) are regarded as 'experts by experience' in the scope of their first-hand experience with a diagnosis or health condition. Research suggests that including PWLE in a patient's care and treatment has significant contributions to the patient's treatment and overall outcome. However, there is minimal evidence of including PWLE in collaborative care models. This paper describes the inclusion of a PWLE in a research study and collaborative care team for youth with early psychosis. Aims To discuss the active involvement of a PWLE on the research and collaborative care team and to describe the research team's experiences and perspectives to facilitate future collaborations. Method This paper describes the inclusion of a PWLE on our research team. We provide a selective review of the literature on several global initiatives of including PWLE in different facets of the healthcare system. Additionally, we outline multiple challenges of involving PWLE in research and service delivery. Examples are provided on how recruitment and involvement was facilitated, with the guidance of several principles. Lastly, we have included a narrative note from the PWLE included in our study, who is also a contributing author to this paper (AA), where she comments on her experience in the research study. Conclusion Including PWLE in active roles in research studies and collaborative care teams can enhance the experience of the researchers, collaborative care team members, and PWLE. We showcase our method to empower other researchers and service providers to continue to seek guidance from PWLE to provide more comprehensive, collaborative care with better health outcomes for the patient, and a more satisfying care experience for the provider.

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)0,034
score de la tête « metaresearch » (Gemma)0,026
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: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,181

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0340,026
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,001
Études des sciences et des technologies0,0140,006
Communication savante0,0070,007
Science ouverte0,0030,014
Intégrité de la recherche0,0040,008
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 Opus0,602
Tête enseignante GPT0,542
Écart entre enseignants0,060 · 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

Citations59
Publié2021
Routes d'admission2
Résumé présentoui

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