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.
Notice bibliographique
Résumé
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.
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 | 0,007 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».