Mental health of LGBTQ+ people during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
Objective The purpose of this study was to analyse available literature on the mental health of LGBTQ+ people during the COVID-19 pandemic.Methods For this scoping review, six electronic databases were searched in three broad topic areas (the COVID-19 pandemic, LGBTQ+ people, mental health) in April 2022. The search yielded 4,285 studies, and after screening them, 61 studies were included in the final review, which were thematically analysed.Results Results were organised according to four themes: comparative effects of the pandemic on LGBTQ+ and heterosexual cisgender people’s mental health; mental health impacts; differences in the mental health among LGBTQ+ people; and protective and risk factors. LGBTQ+ people’s mental health was disproportionately impacted by the pandemic, particularly when compared to heterosexual and cisgender people. Some sub-populations of LGBTQ+ people were impacted more than others, such as bisexual, transgender, non-binary, and gender-diverse people. Depression, anxiety, and stress/distress were the most salient mental health issues, though loneliness, suicidal ideation, self-harm, and COVID-related fears were also prevalent. LGBTQ+ people used substances, social media, dating apps, and pornography to cope with the pandemic.Discussion Gaps in the literature and study limitations are identified, and recommendations for policy, health services, and future research are offered.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".