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Record W2313408612 · doi:10.2105/ajph.2016.303088

Lifetime Prevalence of Suicide Attempts Among Sexual Minority Adults by Study Sampling Strategies: A Systematic Review and Meta-Analysis

2016· review· en· W2313408612 on OpenAlexafffundabout
Travis Salway, Laura Bogaert, Anne E. Rhodes, David J. Brennan, Dionne Gesink

Bibliographic record

VenueAmerican Journal of Public Health · 2016
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsPublic Health Ontario
FundersOntario HIV Treatment Network
KeywordsSexual minorityPopulationDemographyLesbianPsycINFOMeta-analysisCINAHLSuicidal ideationMedicineContext (archaeology)HeterosexualityPsychologyPoison controlClinical psychologyHomosexualityMEDLINESuicide preventionPsychiatryEnvironmental healthPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Previous reviews have demonstrated a higher risk of suicide attempts for lesbian, gay, and bisexual (LGB) persons (sexual minorities), compared with heterosexual groups, but these were restricted to general population studies, thereby excluding individuals sampled through LGB community venues. Each sampling strategy, however, has particular methodological strengths and limitations. For instance, general population probability studies have defined sampling frames but are prone to information bias associated with underreporting of LGB identities. By contrast, LGB community surveys may support disclosure of sexuality but overrepresent individuals with strong LGB community attachment. OBJECTIVES: To reassess the burden of suicide-related behavior among LGB adults, directly comparing estimates derived from population- versus LGB community-based samples. SEARCH METHODS: In 2014, we searched MEDLINE, EMBASE, PsycInfo, CINAHL, and Scopus databases for articles addressing suicide-related behavior (ideation, attempts) among sexual minorities. SELECTION CRITERIA: We selected quantitative studies of sexual minority adults conducted in nonclinical settings in the United States, Canada, Europe, Australia, and New Zealand. DATA COLLECTION AND ANALYSIS: Random effects meta-analysis and meta-regression assessed for a difference in prevalence of suicide-related behavior by sample type, adjusted for study or sample-level variables, including context (year, country), methods (medium, response rate), and subgroup characteristics (age, gender, sexual minority construct). We examined residual heterogeneity by using τ(2). MAIN RESULTS: We pooled 30 cross-sectional studies, including 21,201 sexual minority adults, generating the following lifetime prevalence estimates of suicide attempts: 4% (95% confidence interval [CI] = 3%, 5%) for heterosexual respondents to population surveys, 11% (95% CI = 8%, 15%) for LGB respondents to population surveys, and 20% (95% CI = 18%, 22%) for LGB respondents to community surveys (Figure 1). The difference in LGB estimates by sample type persisted after we accounted for covariates with meta-regression. Sample type explained 33% of the between-study variability. AUTHOR'S CONCLUSIONS: Regardless of sample type examined, sexual minorities had a higher lifetime prevalence of suicide attempts than heterosexual persons; however, the magnitude of this disparity was contingent upon sample type. Community-based surveys of LGB people suggest that 20% of sexual minority adults have attempted suicide. PUBLIC HEALTH IMPLICATIONS: Accurate estimates of sexual minority health disparities are necessary for public health monitoring and research. Most data describing these disparities are derived from 2 sample types, which yield different estimates of the lifetime prevalence of suicide attempts. Additional studies should explore the differential effects of selection and information biases on the 2 predominant sampling approaches used to understand sexual minority health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.067
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.034
Bibliometrics0.0110.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.192
GPT teacher head0.484
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations417
Published2016
Admission routes3
Has abstractyes

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