LONELINESS AND DEPRESSION AMONG SEXUAL AND GENDER MINORITY OLDER ADULTS WITH COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Sexual and gender minority (SGM) older adults are at elevated risks of cognitive impairment (CI). High rates of loneliness and depression are particularly concerning for SGM older adults living with CI as they are linked to decreased physical health, lower life satisfaction, and accelerated cognitive decline. Yet little is known about the risk and protective factors of loneliness and depression for SGM older adults with CI. This study, utilizing the Health Equity Promotion Model, analyzes the Aging with Pride: National Health, Aging, and Sexuality/Gender Study to examine socioeconomic status, adverse life course experiences, and psychological and social factors that predict depressive symptomatology and the mediating role of loneliness. Data were a sub-sample of 160 participants with CI assessed by the Montreal Cognitive Assessment. Approximately 50% reported feeling isolated from others, 33% were experiencing depression, 40% reported physical or sexual child abuse, 70% were single, and 25% had a network size of 3 or less. A bootstrapped mediation analysis using Mplus revealed that child abuse, day-to-day discrimination, perceived stress, not being partnered, reduced network size, and lack of social support and community engagement were indirectly associated with depressive symptomatology via loneliness. Those with adverse childhood experiences, ongoing distressing stigma, and social isolation are of major concern. Improving support networks may reduce the risk of loneliness and depression. These findings can be utilized to develop culturally tailored interventions to enhance the mental health and overall quality of life of this understudied and underserved population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".