Investigating the Psychosocial Impact of COVID-19 Among the Sexual and Gender Minority Population: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: The purpose of this study was to utilize a systematic review and meta-analysis to assess the existing body of literature to understand the mental health impacts of the coronavirus disease-19 (COVID-19) pandemic among sexual and gender minority (SGM) people. Methods: The search strategy was developed by an experienced librarian and used five bibliographical databases, specifically PubMed, Embase, APA PsycINFO (EBSCO), Web of Science, and LGBTQ+ Source (EBSCO), for studies (published 2020 to June, 2021) examining the psychological impact of the COVID-19 pandemic among SGM people. Articles were screened by two reviewers. The quality of the articles was assessed using the National Institutes of Health quality assessment tool for observational studies. A double extraction method was used for data abstraction. Heterogeneity among studies was assessed by I 2 statistic. The random-effects model was utilized to obtain the pooled prevalence. Publication bias was assessed by Funnel plot and Egger's linear regression test. Results: Of a total of 37 studies, 15 studies were included in the meta-analysis with 17,973 SGM participants. Sixteen studies were U.S. based, seven studies were multinational studies, and the remaining studies were from Portugal, Brazil, Chile, Taiwan, the United Kingdom, France, Italy, Canada, and several other countries. A majority of studies used psychometric valid tools for the cross-sectional surveys. The pooled prevalence of anxiety, depression, psychological distress, and suicidal ideation was 58.6%, 57.6%, 52.7%, and 28.8%, respectively. Conclusions: Findings of this study serve as evidence to develop appropriate interventions to promote psychological wellbeing among vulnerable population subgroups, such as SGM individuals.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".