Adapting a Patient-Reported Outcome Measure to Digital Outpatient Specialist Health Care Services for Type 1 Diabetes: User Involvement Study
Notice bibliographique
Résumé
BACKGROUND: Diabetes self-management is crucial for patients with type 1 diabetes, and digital services can support their self-management and facilitate flexible follow-up. The potential of using digital patient-reported outcome (PRO) measures in routine outpatient care is not fully used owing to a lack of adapted PRO measures. OBJECTIVE: This study presents the process of identifying and adapting a digital PRO measure for use in clinical diabetes practice and describes the preferred item topics of the adapted PRO measure, as reported by patients and diabetes specialist nurses. METHODS: With the involvement of patients, diabetes specialist nurses, management, and researchers, we hosted a series of workshops and 2 dialogue conferences. Scoping searches to identify relevant PRO measures formed the foundation for the process. An in-person dialogue conference was conducted with diabetes specialist nurses as participants, and a digital dialogue conference was conducted with patients with type 1 diabetes as participants. A diabetes-specific PRO measure was translated and adapted to our digital platform. Notes and summaries from the dialogue conferences were imported into NVivo (QSR International) and thematically analyzed as a single combined data set. RESULTS: The thematic analysis of the 2 dialogue conferences aimed to explore the views of patients with type 1 diabetes and diabetes specialist nurses on the outcomes necessary to measure. An overarching theme, Ensuring that the PRO measure captures the patients' needs precisely and accurately, in a way that facilitates care and communication with health care personnel, was identified and supported with data from both the patients and diabetes specialist nurses. This theme contained four categories: The need for explanatory text after questions to ensure understanding and accurate response, Capturing individual needs in standardized questions, getting to the heart of the patient's problem, and The questions increase patient reflection. CONCLUSIONS: We successfully conducted an iterative process that identified a PRO measure aligned with the topics raised by the diabetes specialist nurses. Similarly, the patients found the PRO measure to be relevant and one that was addressing their needs. Only minor adjustments were necessary when programming the PRO measure in the digital platform. Our management, patients, and diabetes specialist nurses had a valuable impact on the results. User involvement facilitated a specific focus on the clinical requests to be met by PRO measures and how they must be adapted to local and digital platforms. Overall, this has facilitated the current implementation of the adapted digital PRO measure.
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 | 0,058 | 0,120 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».