Creating a patient reported outcome measure for medication-related quality of life: a concept mapping brainstorming study in Ontario, Canada
Notice bibliographique
Résumé
Introduction: Persons with mobility limitations generally take multiple medications to manage their conditions; however, healthcare providers across different settings do not currently have a way to measure the impact that medication taking has on patients’ quality of life (QoL). Current tools are limited, narrowly focused on few QoL domains and none focus on persons with mobility limitations. Target Audience: The target audience for our study findings are healthcare providers working with individuals with mobility limitations across different healthcare settings. Who was involved: This study included patient and caregiver partners. They were involved in the grant application, study participant recruitment, data collection, and final data analysis. This study also involved healthcare providers who supported participant recruitment and will be knowledge users, including pharmacists, physicians, and physical therapists. Aims and Methods: The aim of this study was to identify a list of potential items for the creation of a patient reported outcome measure (PROM) from the perspectives of individuals with mobility limitations who take medications. We used a mixed methods concept mapping approach to gather perspectives from across Canada. Here, we report on the results of the qualitative brainstorming phase of the study. Participants created a list of statements reflecting what matters to them about their medications in their everyday life. The statements were de-duplicated and condensed, following which, they were thematically analyzed and mapped onto common quality of life domains. Results: Twenty-two persons with mobility limitations across Canada participated in this study. Six hundred and ninety-four statements were condensed into 80 final statements. The final statements mapped onto common quality of life domains (physical health; mental health; employment/social/leisure activities; daily activities; interactions with providers; navigating the healthcare system; autonomy and decision-making; financial health) indicating a comprehensive list of items for the creation of the PROM. In addition, we identified some mobility limitation specific concepts relating to barriers to physically accessing medications and necessary testing, and to perceived health plan or policy restrictions. Learnings for the international audience: Results from this study emphasized the importance of including patient autonomy, decision-making and financial concerns, which are often excluded from other measures related to medication taking and QoL. Additionally, we identified other topics that matter to individuals such as impact on lifestyle activities, patient self-management, ease of access to medications and necessary testing, as well as financial challenges associated with medication taking. Next Steps: The next phases of concept mapping will be conducted, which will identify statements that are important and realistic to individuals with mobility limitations and a final conceptual mapping session. Statements that are rated highly on both scales will inform the development of items for the PROM, which will be validated in future work. In developing and validating a PROM for medication-related QoL, we aim to provide clinicians with an invaluable tool to enhance and monitor clinical care and patient outcomes. We envision this PROM might be used to inform overall medication management.
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 | 0,009 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,012 | 0,003 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».