COVID-19 News and Its Association With the Mental Health of Sexual and Gender Minority Adults: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Sexual and gender minority (SGM; people whose sexual orientation is not heterosexual or whose gender identity varies from what is traditionally associated with the sex assigned to them at birth) people experience high rates of trauma and substantial disparities in anxiety and posttraumatic stress disorder (PTSD). Exposure to traumatic stressors such as news related to COVID-19 may be associated with symptoms of anxiety and PTSD. OBJECTIVE: This study aims to evaluate the relationship of COVID-19 news exposure with anxiety and PTSD symptoms in a sample of SGM adults in the United States. METHODS: Data were collected between March 23 and August 2, 2020, from The PRIDE Study, a national longitudinal cohort study of SGM people. Regression analyses were used to analyze the relationship between self-reported news exposure and symptoms of anxiety using the Generalized Anxiety Disorder-7 and symptoms of COVID-19-related PTSD using the Impact of Events Scale-Revised. RESULTS: Our sample included a total of 3079 SGM participants. Each unit increase in COVID-19-related news exposure was associated with greater anxiety symptoms (odds ratio 1.77, 95% CI 1.63-1.93; P<.001) and 1.93 greater odds of PTSD (95% CI 1.74-2.14; P<.001). CONCLUSIONS: Our study found that COVID-19 news exposure was positively associated with greater symptoms of anxiety and PTSD among SGM people. This supports previous literature in other populations where greater news exposure was associated with poorer mental health. Further research is needed to determine the direction of this relationship and to evaluate for differences among SGM subgroups with multiple marginalized identities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".