Caregiver burden and depression among lesbian, gay, bisexual, and other nonheterosexual individuals in the United States: Analysis of BRFSS 2015–2018
Bibliographic record
Abstract
Abstract The number of caregivers in the US continues to rise. However, the epidemiology and mental health among lesbian, gay, bisexual, and other nonheterosexual (LGB+) caregivers remain unknown. We aimed to characterize the epidemiology of caregiving burden/information among LGB+ caregivers in the United States, as well as to examine depression and related risk factors. By using the data from the 2015–2018 Behavioral Risk Factor Surveillance System (BRFSS), we conducted weighted hierarchical logistic regression models to explore the associations between depression, caregiving information, health behaviors, sex, and sexual orientation among an estimated population of 9,521,313 LGB+ caregivers. Among the caregivers aged 18–79 years, 4.81% identified as LGB+. Notably, 19.21% of the caregivers reported experiencing depression, with distinctive rates observed among male LGB+ caregivers and female LGB+ caregivers were 38.22% and 51.43%, respectively. Meanwhile, nearly 45.00% of male LGB+ caregivers offered care to nonrelatives, a significantly higher rate compared to their heterosexual counterparts (23.32%, p < 0.001). The logistic regression models revealed that both male LGB+ caregivers (odds ratio [OR]: 3.56; 95% confidence interval [CI]: 1.45–8.77) and female LGB+ caregivers (OR: 2.38; 95% CI: 1.00–5.63) exhibited a higher likelihood of reporting depression compared to male heterosexual peers. Additionally, caregivers seeking support services for their caregiving responsibilities were more likely to report depression (OR: 1.47; 95% CI: 1.12–1.94). Our study revealed higher caregiving burden and depression among LGB+ caregivers in the United States, warranting further research and targeted support to address their unique challenges and improve services to caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".