Virtual Care, What Are We Measuring and What Should We Measure? Scoping Review of Reviews
Notice bibliographique
Résumé
BACKGROUND: Virtual care is here to stay but there remains no comprehensive measurement framework to guide evaluation of its impacts, to inform policy decisions and optimization of practice. OBJECTIVE: The aim of our study was to conduct a scoping review of reviews to synthesize measures related to virtual care evaluation across clinical conditions and contexts to identify gaps in current evaluation measures, and to inform the development of recommendations for future work. METHODS: Citations published from 2015-2023 were retrieved from Medline, Cochrane Database of Systematic Reviews, Embase, Emcare, Scopus, CINAHL and Web of Science, utilizing search terms grouped by key concept (virtual care and evaluation/ quality measurement). Measures were defined as any quantitative or qualitative evaluation of performance or impact of virtual care on processes, outcomes or systems. Articles were excluded if they were not a literature review (primary results, commentaries, letters, protocols), dealt exclusively with pediatric populations, published in a language other than English, or were abstract only. Measures from retained articles (1,233) were thematically grouped against the Proctor Implementation Research Outcomes framework. The study was reported according to the PRISMA guideline extension for scoping reviews. RESULTS: There has been substantial growth in the virtual care literature, particularly since the start of the COVID-19 pandemic. The majority of articles (73.0%; 900/1233) evaluated client outcomes, including satisfaction with virtual care, usability or functionality of platforms, and/or clinical outcomes. Relative to the other domains of the Proctor framework, implementation measures were poorly defined, and many of the measures were proxy rather than direct measures. Despite the potential impacts of virtual care on health equity, most studies examining health equity were purely qualitative. Measures of safety, privacy and security of virtual care were sparse and poorly defined. Caregivers play an important role in facilitating virtual visits and providing informal technical support; however, few studies examined implementation or satisfaction with virtual care from the perspective of caregivers. Additionally, clinician experience and acceptance of virtual care has implications for availability and adoption; however, relative to patients, few articles examined the clinician perspective. CONCLUSIONS: Our study highlights gaps in current evaluations of virtual care. Work is needed to improve the quality and standardization of virtual care evaluation to ensure reproducibility, generalizability and comparability of findings. Additionally, better compliance with existing measure definitions and conventions should extend to virtual care. Finally, additional theoretical work is needed to standardize and conceptually frame future virtual care evaluations. Future studies should include both the caregiver and clinician as unique perspectives in evaluations, and should embed systematic evaluations of the impact of social determinants of health on virtual care access, adoption, and perspectives of 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 | 0,150 | 0,467 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,012 | 0,010 |
| Bibliométrie | 0,037 | 0,033 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,014 | 0,020 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,006 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».