Think aloud and tell us everything that comes to mind”: Results from cognitive testing of a patient-reported experience measure (PREM) for integrated home and community care.
Bibliographic record
Abstract
Background: Ontario’s health system goal is to put clients at the centre with the right care, at the right time, in the right place. To do this there is a need to integrate home care with community-based services. This type of integration should, in theory, better support people to continue living well, with dignity and safety, in their homes and communities for as long as possible. A critical component of improving integrated home and community care is gathering self-reported data of client experience, through patient-reported experience measures (PREMs). When client reported data is integrated into health system improvement initiatives, there is a trend towards better health outcomes, including the adoption of safe practices, better communication, and improved clinical indicators. However, no PREM currently exists to measure client experience of integrated home and community care. Aims: We aimed to engage clients of home care services and family/friend caregivers to clients receiving home care in Ontario, Canada to tells us about the usability of a newly developed PREM for emerging models of integrated home and community care. This engagement should ensure the PREM is easy to comprehend and straightforward, making it more likely to generate reliable and valid information . Methods: We engaged with clients and caregivers (n=10) with diverse gender expressions, racial backgrounds, abilities, and socioeconomic status in one-to-one interviews to identify issues related to answering the questions on our newly developed PREM. Participants were asked to “think aloud and tell us everything that comes to mind, whether it seems important or not” while completing the PREM. This approach elicited information about the clarity of the instructions, their understanding of the questions, the item stem and scale match, and what led them to their answer. We asked additional question related to comprehension of terms, ambiguity, value-laden words, double-barreled questions, positive and negative wording, and length. Two members of the research team conducted thematic analysis of the generated transcripts and came to consensus regarding required changes to the scaling and items. Results: At the time of the conference, the detailed results of the cognitive testing will be available. We anticipate sharing recommendations from users of home care on 1) creating clear scaling options, 2) wording items clearly, 3) the appropriate length of the questions and overall survey, and 4) clarifying jargon or unintended meaning of questions. The finalized draft PREM will be shared. Learnings: Results will provide insight into how to clarify wording and scale a survey about client experience of integrated home and community. This will help to improve survey design and user experience, ultimately enhancing potential for a reliable and valid measure of patient experience. Next steps: The PREM will be adapted and psychometrically tested. If found to be reliable and valid for use in home and community care, it will be rolled out at a home care service provider organization across Canada in late 2023. This PREM data should be integrated with other metrics of the quadruple aim to best guide healthcare improvement.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".