Prevalence of Mental Health and Substance Use Difficulties Among Sexual and Gender Diverse Youth During COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose:Sexual and gender diverse (SGD) youth have been particularly vulnerable to mental health difficulties and substance use during the COVID-19 pandemic. However, estimates have varied across studies pointing to the potential for moderator variables. This meta-analytic and narrative synthesis provides estimates of the prevalence of mental health difficulties (anxiety, depression, suicidal ideation, suicide attempts) and substance use during COVID-19 among SGD youth. Methods:A comprehensive search strategy combining keywords and subject headings was designed and used across eight databases from inception to October 7, 2022. The search yielded 826 nonduplicate records of which 191 full-text articles were retrieved, evaluated, and extracted by two study authors. Data were analyzed from February 27 to March 1, 2023. Results:Using random-effects meta-analyses, 19 studies from 18 independent samples with 10,500 participants were included. Pooled prevalence rates for clinically elevated anxiety, depression, and suicidal ideation were 55.4% [95% confidence interval (CI):45.9%–64.5%], 61.8% (95% CI: 50.9%–71.7%), and 50.9% (95% CI: 42.8%–59.0%). There was no evidence of publication bias. Suicide attempts and substance use were summarized narratively with rates of suicide attempts being greater than 20% across included studies and variable reporting of substance use across substance types. No moderators explained variability across studies. Conclusion:More than 50% of SGD youth experienced clinically elevated symptoms of anxiety, depression, and suicidal ideation during the COVID-19 pandemic, compared to prepandemic estimates for both SGD and non-SGD youth. Targeted resource allocation is needed to specifically address the needs of SGD youth.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".