Experience and Impact of COVID-19 on a Newly Formed Rural University Medical Office: Survey Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic had large social effects, particularly in the fields of medicine and medical education. Medical organizations in the United States operate in overlapping contexts with interrelated goals inside multiple organizations, and the context of work strongly influenced how organizations were able to respond to COVID-19 restrictions. OBJECTIVE: This research examines the experience and impact of COVID-19 on the implementation of a Health Resources and Services Administration grant in a newly formed university medical office with the interrelated goals of health policy, health outreach, and medical education. The goal is to understand how COVID-19 created different experiences and challenges for leaders and their collaborators working in medical education compared to those working in public health outreach or health policy. METHODS: A survey about COVID-19 opportunities and challenges was administered to work unit leaders and their project collaborators. The most common experiences and challenges are shown, direct educational and other respondents' experiences and challenges are compared, and open-ended comment segments are analyzed. RESULTS: Helping others adjust to digital work, remoteness, and coordination were common experiences during COVID-19. Common challenges include coordination and an inability to make comparisons to previous program years. On average, respondents had 11.3 (SD 7.8) experiences and 8.3 (SD 6.9) challenges considered in the survey. While all units were influenced by COVID-19 restrictions, medical education units had more experiences and challenges. Those involved directly in medical education experienced 69% (18.6/27) of their possible experiences and 54% (14.7/27) of their possible challenges on average compared to 35% (7/20) and 21% (4.2/20) among other respondents (P<.001). COVID-19 restrictions increased the complexity of project work and presented challenges, especially in terms of coordinating responses and access to locations. CONCLUSIONS: The findings suggest that COVID-19 made the overall administration of programs more complex and drew attention from other medical and public health programs. While remoteness is appropriate for some medical education tasks, it is less appropriate for clinical learning. Remoteness presents an especially large challenge to clinical education. Employees now have expectations for remoteness to be built into programs and workplaces. Program administrators will have to integrate remoteness' benefits and drawbacks into their organization for the foreseeable future.
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 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».