Prevalence and associated factors of post-traumatic stress disorders among LGBTQI+ adults: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: LGBTQI+ individuals appear to be particularly at-risk of exposure to interpersonal violence. This leads to an increased risk of developing symptoms of Post-Traumatic Stress Disorder (PTSD) or Complex Post-Traumatic Stress Disorder (C-PTSD). The objectives of this systematic review is to: (1) Compile the prevalences of PTSD/C-PTSD among LGBTQI+ individuals; (2) Compare the symptomatology of PTSD/C-PTSD according to sexual orientation and gender identity; (3) Identify the factors involved in the symptomatology of PTSD/C-PTSD among LGBTQI+ individuals. METHODS: A systematic literature review was conducted on PTSD/C-PTSD among LGBTQI+ individuals. Psychinfo, Psycharticle, Psychology and Behavioral Sciences Collection, Embase, and LGBTHealth databases were queried. Only quantitative, observational studies based on data collected after 2010 and involving LGBTQI+ adults were included. The risk of bias was assessed using the Control Guidelines Critical Appraisal Toolkit developed by the Public Health Agency of Canada. RESULTS: Out of the 7446 articles identified, 60 were included. Eighteen provided data on prevalence, and 57 on associated factors. The majority of studies were conducted in the United States. The vast majority of studies assessed PTSD using self-administered scales. Only one evaluated symptoms of C-PTSD. All included studies reported extremely high PTSD prevalence rates, with certain populations appearing particularly at risk, such as bisexual (10.3-35.7% PTSD) and transgender individuals (36.8-64.3% PTSD). Individual (e.g., financial precarity, transition, internalized stigma), interpersonal (e.g., outness, social support), organizational (e.g. health barriers), community (e.g. anti-trans discourse), and political variables (anti-trans laws project) associated with PTSD/C-PTSD symptoms have been identified. CONCLUSIONS: The results show the importance of considering PTSD/C-PTSD among LGBTQI+ individuals in clinical practice and research. Higher quality studies are needed to quantify the extent of the problem. Healthcare professionals must be trained in the specific factors that may contribute to PTSD/C-PTSD symptoms in LGBTQI+ individuals. This cannot be achieved without public policies aimed at preventing all forms of violence against the LGBTQI+ population and ensuring access to equal rights.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".