Primary Care Provider Experiences and Perspectives of Virtual Primary Care Visits
Notice bibliographique
Résumé
Context: The onset of COVID-19 and rapid response to public health restrictions prompted increased use of virtual care (VC). Prior to the pandemic there was low utilization of technology for communication and many primary care providers (PCPs) had little to no experience with VC. Thus, greater use of VC required adjustments to how health care was provided. Literature specific to VC has focused on communication modalities with lingering questions regarding how providers have been impacted. Objective: To explore virtual care (VC) adoption and use in Manitoba, Canada from the perspective of health care providers. Study Design and Analysis: Qualitative phenomenological approach using content analysis performed by two members of the team including a patient partner. Setting: Six focus group sessions conducted virtually. Population Studied: 21 primary care providers in Manitoba, Canada. Intervention/instrument: Exploration of experiences including benefits and challenges of VC, the impacts on provider workload, quality of care and clinic workflow, as well as recommendations for sustainable VC. Results: Options for VC visits were limited due to logistical and accessibility challenges faced by providers and patients. Telephone visits were most common. In some instances, VC was useful for screening and assessment; however, the lack of visual cues challenged the delivery of care. Respondents felt consults required in-depth history-taking and focused exploratory questioning, but also raised the concern of having to balance risk and ruling out more serious conditions. One provider referred to VC as “a great addition to the whole care package,” generally offering convenience and greater accessibility for some but limitations for others. Providers experienced more flexibility with their practice, which benefitted their well-being and evolved as providers developed individual strategies for the ‘right mix’ of virtual and in-person care. Conclusion: The perspectives gained from one of the key ‘user’ groups within the health care system will likely resonate with health care providers across Canada and beyond, who also were faced with implementing VC in a rapidly changing environment during the pandemic. Through the experiences of health care providers, we gain a better understanding of VC within clinical practice; where challenges need to be mitigated; and the recommendations for sustained quality VC beyond the pandemic era.
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,005 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».