Food insecurity is associated with depression and diabetes among sexual minority adults:A preliminary analysis of syndemic effects
Bibliographic record
Abstract
Background: The impact of the coronavirus disease 2019 (COVID-19) pandemic and the public health measures put in place to control COVID-19 transmissions (e.g., social distancing, self-isolation) is heterogenous, with greater consequences among populations experiencing social, economic, and political marginalization, including members of the 2-Spirit, Lesbian, Gay, Bisexual, Trans, Queer, (2SLGBTQ+) community. Methods: Using a biocultural approach, we examine the mental health impacts of the COVID-19 pandemic using data from a mixed methods study of 455 self-identified 2SLGBTQ+ adults living in Toronto, Ontario, Canada, collected from March 2021 to July 2021. Participants were recruited using respondent-driven sampling to complete an internet-based survey including measures of psychological distress and minority stress. A subset of participants (n =50) completed a semi-structured qualitative interview to contextualize their mental health experiences during the COVID-19 pandemic. Results: Bivariate analysis revealed that self-reported anxiety (P= 0.029), depression (P = 0.001), perceived stress (P = 0.002), and somatic symptom scores (P = 0.014) were elevated among participants who reported living with family during the pandemic. These differences persisted in regression analysis adjusting for sexual identity, gender expression, race/ethnicity, age, citizenship, education, household size, and income. Discussion: Our findings offer important insights that will enable Toronto and other Canadian public health agencies to better respond to the needs of 2SLGBTQ+ and other vulnerable communities during ongoing COVID-19 pandemic and future health crises.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".