Development, testing and international opportunities for a novel Patient Reported Experience Measure (PREM) of integrated home and community care
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".