Utilization of International Medical Graduates (IMGs) for COVID-19 Response in Multicultural Communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".