Exploring Patient Advisors’ Perceptions of Virtual Care Across Canada: Qualitative Phenomenological Study
Notice bibliographique
Résumé
BACKGROUND: While virtual care services existed prior to the emergence of COVID-19, the pandemic catalyzed a rapid transition from in-person to virtual care service delivery across the Canadian health care system. Virtual care includes synchronous or asynchronous delivery of health care services through video visits, telephone visits, or secure messaging. Patient advisors are people with patient and caregiving experiences who collaborate within the health care system to share insights and experiences in order to improve health care. OBJECTIVE: This study aimed to understand patient advisors' perceptions related to virtual care and potential impacts on health care quality. METHODS: We adopted a phenomenological approach, whereby we interviewed 20 participants who were patient advisors across Canada using a semistructured interview protocol. The protocol was developed by content experts and medical education researchers. The interviews were audio-recorded, transcribed verbatim, and analyzed thematically. Data collection stopped once thematic saturation was reached. The study was conducted at Queen's University, Kingston, Ontario. We recruited 20 participants from 5 Canadian provinces (17 female participants and 3 male participants). RESULTS: Six themes were identified: (1) characteristics of effective health care, (2) experiences with virtual care, (3) modality preferences, (4) involvement of others, (5) risks associated with virtual care encounters, and (6) vulnerable populations. Participants reported that high-quality health care included building relationships and treating patients holistically. In general, participants described positive experiences with virtual care during the pandemic, including greater efficiency, increased accessibility, and that virtual care was less stressful and more patient centered. Participants comparing virtual care with in-person care reported that time, scheduling, and content of interactions were similar across modalities. However, participants also shared the perception that certain modalities were more appropriate for specific clinical encounters (eg, prescription renewals and follow-up appointments). Perspectives related to the involvement of family members and medical trainees were positive. Potential risks included miscommunication, privacy concerns, and inaccurate patient assessments. All participants agreed that stakeholders should be proactive in applying strategies to support vulnerable patients. Participants also recommended education for patients and providers to improve virtual care delivery. CONCLUSIONS: Participant-reported experiences of virtual care encounters were relatively positive. Future work could focus on delivering training and resources for providers and patients. While initial experiences are positive, there is a need for ongoing stakeholder engagement and evaluation to improve patient and caregiver experiences with virtual care.
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 | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,026 | 0,014 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».