Analyzing facilitators and barriers to telehealth in rural North America during the covid-19 pandemic: A scoping review
Notice bibliographique
Résumé
Background: It has previously been established that many people living in rural communities experience health disadvantages and less access to medical care. Over the past decade, innovations in telehealth and other innovative models of care have been developed with the goal of overcoming these inequities for those living in rural areas. Objective: The aim of this paper was to describe both outcomes and characteristics of studies involving telehealth in rural areas of North America during the COVID-19 pandemic. Materials and Methods: A scoping review was undertaken. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was utilized in order to understand the empirical and theoretical data on telehealth usage in the United States, Canada and Mexico during the COVID-19 pandemic. The following terms were utilized: ‘rural health’ AND ‘telehealth’ AND ‘covid-19’. Separate searches were completed for the three included countries: ‘United States’, ‘Canada’, and ‘Mexico’. PubMed and Google Scholar were utilized. Results: The literature search revealed 1197 articles published in English between 1st January 2019 and 31st August 2022. One hundred and fifty articles were included in the review including 135 from the United States, 12 from Canada, and 10 from Mexico. Some articles were cross-collaborations between two of these countries. Among these papers, 18% (27) focused on telemedicine for mental health treatments, 14.7% (22) focused on oncology or cancer, 11.3% (17) focused on telemedicine for the veteran subpopulation, 2.7% (4) used a mixed methods approach, and 14% (21) used a qualitative approach. Conclusion: This scoping review reveals that the current literature on telehealth in rural areas during the COVID-19 pandemic is largely descriptive. There were only a few publications that focused on comparative health outcomes using telehealth in urban and rural populations in close proximity to each other. Telehealth is well represented in published literature on inequities and innovation, but there is still limited data on health outcomes and comparisons that can be drawn cross-nationally. Further studies should aim to study longer term health outcomes for those in rural areas using telehealth as opposed to areas where telehealth interventions have not yet been adopted
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 | 0,016 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,012 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».