Identifying what matters to Canadian adults with mobility limitations regarding experiences with medications: A concept mapping study
Notice bibliographique
Résumé
Background: Persons with mobility limitations generally take multiple medications to manage their condition and other health complications. There are limited assessment tools measuring the experiences with medications and their impacts on everyday life. Understanding what matters to persons about experiences with medications will inform person-centred clinical care, integrated care, ongoing monitoring, and overall quality of care. Objective: The objective of this study was to identify what matters to Canadian adults with mobility limitations regarding their experiences with medications. Methods: We conducted a concept mapping study which is a participatory, mixed methods approach. It involves six steps: preparation, brainstorming, sorting and rating, analysis, mapping and interpretation, and utilization. Participants were required to: be 18 years of age or older, live in Canada, live in the community, speak and read English or French, have a mobility limitation, and take at least one medication recommended by a prescriber in the preceding three months. During the brainstorming sessions, participants generated statements in response to the focal prompt: what matters to you about medications in your everyday life? In the sorting task, participants created piles of statements based on their conceptual similarity. In the rating task, participants rated each statement on two dimensions – importance and realistic. In the mapping session, a subset of participants created visual maps of the data. Results: Participants generated 694 statements during the brainstorming sessions, which were synthesized into a final list of 80 statements. The final map contained ten clusters that aligned with what mattered to participants about their medications in everyday life: (1) medication-related financial considerations and support; (2) pharmacy-related services and supports; (3) access to medications and medication-related supports; (4) acceptance and stigma around medication use; (5) ability and ease of taking medications; (6) shared decision-making and access to medication-related research and information; (7) medication effectiveness, side effects and risks; (8) knowledge, self-awareness and empowerment; (9) accessibility of healthcare providers; and (10) communication and relationships with healthcare providers. Medication-related financial considerations and support was the cluster rated highest on importance, but lowest on realistic. Implications and Next Steps: This research has identified key items and domains related to medication-related experiences that will inform improved healthcare delivery and outcomes for Canadian adults who take medications. In the next steps of this research, we will engage with medication prescribers, administrators, decision-makers, and patients to better understand implementation considerations around patient-reported experience and/or outcome measures, prior to the development of a measure to be used in practice. Conclusions: There is currently a lack of patient-reported experiences and/or outcome measures that apply a comprehensive assessment on the experiences with or impact of medications on everyday life. Obtaining a better understanding of and individuals’ experiences with medications and how they may impact their quality of life will help inform the co-development and implementation of an experience measure specific to medications.
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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 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,015 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| 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 ».