T245. A MODEL 2.0 FOR EARLY INTERVENTION SERVICES FOR PSYCHOSIS: USING A LEARNING HEALTHCARE SYSTEM APPROACH TO IMPROVE EVIDENCE-BASED CARE
Notice bibliographique
Résumé
Abstract Background In Canada, 26.3% of people reporting having mental disorders have indicated that they did not receive adequate care for their mental illness. However, early and evidence-based treatment can significantly reduce the severity of mental illnesses. Early Intervention Services (EIS) for psychosis are an example of such an intervention. EIS are widely recognized as a more effective treatment than routine care for early psychosis. Most Canadian EIS for psychosis follow recommendations on clinical components of care (i.e., easy and rapid access, a case management team approach); however, evidence-based interventions (e.g., measurement-based care or integrated psychosocial interventions) are not always available. Overall, various barriers limit the provision of quality care in the mental health sector, including EIS for psychosis treatment. These barriers include insufficient funding at a time of increasing demand; lack of services; lack of evidence- and measurement-based treatments; and insufficient training for staff and resources for patients. Innovative solutions are required. This presentation describes how e-mental health (eMH) technologies can mitigate these barriers, thus increasing access to evidence-based treatments. Methods Using a learning healthcare system approach, this 2.0 mental health services model aims to (a) identify, describe, and explain the factors affecting the routine incorporation and sustainability of eMH technologies in EIS for psychosis, and (b) optimize the methods associated with the development, adaptation, and evaluation of eMH technologies in real clinical settings. These aims are achieved by implementing three e-MH projects and unpacking the co-design/adaptation process and test the implementation, evaluation, and sustainability of eMH interventions and their effects on patient outcomes. Results The learning healthcare system is considered a new research paradigm able to promote quality, safety, and value in health care. Three project are at the core of this learning healthcare system for psychosis: (1) e-Mental Health Assessment and Monitoring (Project A: DIALOG+/e-Pathways to care): (a) To promote evidence- and measurement-based care in EIS for psychosis and (b) to use such technologies (such as electronic data capture platforms and data visualization) to support shared decision-making during treatment; (2) e-Treatment (Project B: CBT/pathways to care game-based interviews): (a) To facilitate the access and use of e-cognitive behavioral therapy (e-CBT) interventions in EIS for psychosis and (b) to support the treatment of secondary illnesses/comorbidities (depression and anxiety); (3) Web-based Training (Project C e-Training): (a) To co-produce web-based training and evaluate its effects on building capacity for the use of eMH technologies in EIS for psychosis and (b) to deliver psycho-educational interventions and continuing education training through interactive case-based learning. Discussion This work is timely. The innovative use of the rapid learning system approach in EIS for psychosis will offer a unique opportunity for integrating technologies and data into clinical practice, and should bring meaningful benefits to patients and promote Quebec’s open science research.
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 | 0,020 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 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 ».