Chronic pain among U.S. sexual minority adults who identify as gay, lesbian, bisexual, or “something else”
Bibliographic record
Abstract
ABSTRACT: This study assesses chronic pain prevalence among sexual minority U.S. adults who self-identify as gay/lesbian, bisexual, or "something else," and examines the role of select covariates in the observed patterns. Analyses are based on 2013 to 2018 waves of the National Health Interview Survey, a leading cross-sectional survey representative of the U.S. population. General chronic pain and chronic pain in 3+ sites among adults aged 18 to 64 years (N = 134,266 and 95,675, respectively) are analyzed using robust Poisson regression and nonlinear decomposition; covariates include demographic, socioeconomic, healthcare, and psychological distress measures. We find large disparities for both pain outcomes. Americans who self-identify as bisexual or "something else" have the highest general chronic pain prevalence (23.7% and 27.0%, respectively), compared with 21.7% among gay/lesbian and 17.2% straight adults. For pain in 3+ sites, disparities are even larger: Age-adjusted prevalence is over twice as high among adults who self-identify as bisexual or "something else" and 50% higher among gay/lesbian, compared with straight adults. Psychological distress is the most salient correlate of the disparities, whereas socioeconomic status and healthcare variables explain only a modest proportion. Findings thus indicate that even in an era of meaningful social and political advances, sexual minority American adults have significantly more chronic pain than their straight counterparts. We call for data collection efforts to include information on perceived discrimination, prejudice, and stigma as potential key upstream factors that drive pain disparities among members of these minoritized groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".