Challenges and Resiliency: Social Determinants of Health, COVID-19, and the Disproportionate Impact on Immigrants and Refugees Living with HIV
Bibliographic record
Abstract
The human immunodeficiency virus (HIV) pandemic is a global public health and social justice issue. HIV continues to disproportionately affect marginalized populations, including immigrants and refugees living with HIV (IRLHIV). This study investigated and captured the experiences of IRLHIV using the social determinants of health framework. This study examined the intersecting factors affecting the health and well-being of IRLHIV in Alberta, Canada, prior to and during the COVID-19 pandemic. Concurrent mixed methods were used. Employing an online survey (n = 124) and photovoice methodology (n = 13), the researchers identified five salient themes: experiences of racism and discrimination, challenges accessing nutrition, healthcare, and affordable housing, and precarious employment situations. The findings underscored the amplification of pre-existing inequities during the COVID-19 pandemic, intensifying the discrimination and stigma faced by IRLHIV due to both their health status and immigration background. These findings highlight the urgent need for targeted, evidence-based interventions to address the social determinants of health that adversely affect IRLHIV. The researchers recommend further participatory research action into health disparities for IRLHIV to create responsive and culturally safe services for IRLHIV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".