Development, testing and international opportunities for a novel Patient Reported Experience Measure (PREM) of integrated home and community care
Notice bibliographique
Résumé
BACKGROUND: As health systems transition to more integration, it is important to measure whether the aims of integration are being achieved, including health equity and patient experience. One way to do this is by gathering self-reported data through patient-reported experience measures (PREMs). Integrating PREM data into health system improvement initiatives has been shown to improve health outcomes. As health systems around the world seek to expand long-term care capacity in community settings to meet rising demand, new care models are emerging that focus on integrating home and community care and services. To rigorously evaluate these programs and whether they are meaningful to the people they serve, and provide useful feedback for quality improvement, PREM measures are needed; yet we could not locate a PREM specifically tailored to the home and community context. Our team has undertaken a rigorous approach to develop the first PREM of integrated home and community care that measures experiences of equity, life (whole-person) care, continuity and relational caring. The newly-developed PREM focuses on care experience elements that are important to individuals (e.g., trust) and not system processes alone (e.g., discharge). Preliminary psychometric testing of the PREM domains indicates they show excellent internal consistency and moderate reliability. OBJECTIVES: The objectives of this workshop are 1) to provide an overview of the involvement of experts-by-experience in the development and testing of a new PREM that meaningfully captures the experience of clients of integrated models of home and community care; and 2) to explore the applicability of the newly developed PREM to care contexts beyond Ontario, Canada. CONTENT: We will begin with an overview of how the new PREM was developed using a four-phased approach, guided by Streiner et al.’s (2015) method (15 minutes). We will highlight the authentic engagement of experts-by-experience (i.e., clients, family/friend caregivers, health and social care providers and home care leaders) throughout the process, and the rigorous psychometric testing completed. We will then present the results of piloting the PREM with clients of home and community care in Ontario, Canada (5 minutes). Next, we will share the PREM with the audience, and facilitate interactive activities to explore the potential applicability of the PREM’s domains, questions, and format to care contexts beyond integrated home and communicate care in Ontario, Canada (40 minutes). TARGET AUDIENCE: researchers, clinicians, managers, community representatives, home and community care clients and caregivers from around the world who are interested in the development, adaptation and implementation of meaningful, reliable, and valid measures of patient experience with a focus on integrated home and community care. LEARNINGS: Delegates will have a better understanding of the process for developing a new PREM that provides valid, reliable and meaningful patient experience data. In addition, delegates will be able to articulate their vision for and explore their potential interest to apply the new PREM of integrated home and community within their own contexts. REFERENCES: Streiner, D. L., et al. (2015). Health measurement scales: a practical guide to their development and use. Oxford University Press, USA.
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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 | 0,065 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 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 ».