Biosocial Factors Shaping Perceptions of Disease Risk Among a Community‐Based Sample of Sexual and Gender Minority People Living in Toronto During the <scp>COVID</scp> ‐19 Pandemic
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has disproportionately affected vulnerable populations, including sexual and gender minority (SGM) people. Food insecurity, prevalent among this population, may influence perceived vulnerability to infection and related psychological outcomes. This study investigated the association between food insecurity and perceived vulnerability to infection among SGM adults in Toronto, Canada, during the third wave of the COVID-19 pandemic. METHODS: A mixed-methods study was conducted with 338 self-identified SGM adults recruited via respondent-driven sampling to complete an internet-based survey between March and July 2021. Measures included food security status, germ aversion, perceived infectability, and COVID-19 worry. Structural equation modeling (SEM) examined pathways linking food insecurity, discrimination, sleep quality, and perceived vulnerability to disease, adjusting for demographic and socioeconomic covariates. RESULTS: The SEM showed that discrimination predicted increased food insecurity (β = 0.30, p < 0.001) and poorer sleep quality (β = 0.26, p < 0.001). Sleep quality mediated the relationship between food insecurity and perceived vulnerability to disease (indirect effect = 0.16, p < 0.001). Discrimination had a significant total effect on perceived vulnerability to disease (β = 0.22, p < 0.001). DISCUSSION: These findings highlight the roles of food insecurity, discrimination, and sleep quality in shaping perceptions of disease vulnerability and risk among SGM people. Interventions addressing food security, mental health, and structural inequities are crucial for mitigating health disparities both during public health crises and in everyday life.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".