Utilization of International Medical Graduates (IMGs) for COVID-19 Response in Multicultural Communities
Notice bibliographique
Résumé
The dissemination and consumption of misinformation referred to as the ‘infodemic’ spiked exponentially since the COVID-19 pandemic. The Internet, social media, and other communication platforms have eroded traditional health communication strategies by allowing misinformation to diffuse faster than ever before. This infodemic has made public health communication extremely difficult, especially in the multi-cultural Canadian population fabric largely due to language and cultural differences. International Medical Graduates (IMGs) are those who received their medical training outside North America are mostly underutilized. The majority of them are immigrants from various socio-cultural backgrounds. Having formal medical training, years of experience, and diverse backgrounds made them a perfect fit for supporting the COVID-19 response for various ethnic communities in Canada. The Alberta International Medical Graduates Association (AIMGA) is a non-profit organization funded to support the integration of IMGs in their professional integration. At the onset of the pandemic, AIMGA sought opportunities in community where IMGs could provide supports towards the fight against COVID-19. AIMGA was initially called upon by Alberta Health Services to support employees and their families in meatpacking plants where large outbreaks had occurred. AIMGA’s COVID Response team was formed which has grown to include over 125 members. The IMGs have worked as health brokers/navigators in collaboration with newcomer service provider organizations, provincial health service providers, primary care networks, and employers. They have supported activities of the Calgary East-zone Newcomers Collaborative (CENC), ActionDignity, Calgary Catholic Immigration Society (CCIS), and other organizations by providing multi-lingual COVID-19 educational supports, evidence-based vaccine-related information, updates on the changing public health restrictions and the provincial vaccine rollout, along with informational sessions (Q&A sessions, presentations, townhalls) on COVID-19 and the vaccines. They made calls to employees and newcomer clients to address COVID-19 concerns and vaccine hesitancy. They’ve worked in the community and supported vaccine clinics to increase vaccine uptake. AIMGA also supported the onboarding of over 80 IMGs employed by Alberta Health Services as contact-tracers who played a crucial role in limiting the spread of COVID-19 in Alberta. This model of the utilization of IMGs in the community is unique across North America and has proven effective. In this session, we will explore the model further, the impact on the community, lessons learned, and future applications to support communities and our healthcare system.
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 | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».