Effects of Multiple Forms of Information Bias on Estimated Prevalence of Suicide Attempts According to Sexual Orientation: An Application of a Bayesian Misclassification Correction Method to Data From a Systematic Review
Bibliographic record
Abstract
Multiple epidemiologic studies demonstrate a disparity in suicide risk between sexual minority (lesbian, gay, bisexual) and heterosexual populations; however, both "exposure" (sexual minority status) and outcome (suicide attempts) may be affected by information bias related to errors in self-reporting. We therefore applied a Bayesian misclassification correction method to account for possible information biases. A systematic literature search identified studies of lifetime suicide attempts in sexual minority and heterosexual adults, and frequentist meta-analysis was used to generate unadjusted estimates of relative risk. A Bayesian model accounting for prior information about sensitivity and specificity of exposure and outcome measures was used to adjust for misclassification biases. In unadjusted frequentist analysis, the relative risk of lifetime suicide attempt comparing sexual minority with heterosexual groups was 3.38 (95% confidence interval: 2.65, 4.32). In Bayesian reanalysis, the estimated prevalence was slightly reduced in heterosexual adults and increased in sexual minority adults, yielding a relative risk of 4.67 (95% credible interval: 3.94, 5.73). The disparity in lifetime suicide attempts between sexual minority and heterosexual adults is greater than previously estimated, when accounting for multiple forms of information bias. Additional research on the impact of information bias in studies of sexual minority health should be pursued.
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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 | 0.358 | 0.591 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".