THE PROTECTIVE ROLE OF SOCIAL SUPPORT ON DEPRESSIVE SYMPTOMS AMONG AGING SEXUAL MINORITY CANADIANS
Bibliographic record
Abstract
Abstract Sexual minority older adults face minority stressors that are associated with higher rates of mental illness. The stress buffering effects of social support within majority populations are well documented. Using a large population-based sample of aging Canadians, we sought to examine the relationship between sexual orientation and depressive symptoms, and determine whether this relationship is moderated by social support and sex. Baseline data from the Canadian Longitudinal Study on Aging (CLSA) were used (n = 46147). Participants were between the ages of 45-85 years at time of recruitment (mean age = 62.46, SD = 10.27), and self-reported their sexual orientation as heterosexual or lesbian, gay, or bisexual (LGB) (2.1%). Social support and depressive symptoms were measured using validated instruments. Four functional social support subscales were derived: tangible, positive social interaction, affectionate, and emotional/informational. Multiple linear regression models adjusted for relevant covariates were conducted. LGB identification was associated with greater depressive symptoms when compared to heterosexual participants (p = 0.032). As evidenced by a significant 3-way interaction (p = 0.030), increasing tangible social support was associated with a corresponding decrease in the risk of depressive symptoms; this relationship was most pronounced for lesbian and bisexual women. A significant 2-way interaction (p = 0.040) revealed that as emotional/informational social support increased, depressive symptoms decreased, with greater disparity between LGB and heterosexual participants at lower levels of social support. The results highlight the importance of social support in promoting mental health, especially among sexual minority older adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".