Association Between LGB Sexual Orientation and Depression Mediated by Negative Social Media Experiences: National Survey Study of US Young Adults
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, and bisexual (LGB) persons are disproportionately affected by depression and have high social media use rates. Negative social media experiences may modify depressive symptoms among LGB persons. We sought to assess the potential influence of negative social media experiences on the association between LGB orientation and depression. OBJECTIVE: The aim of this study was to assess the potential influence of negative social media experiences on the association between LGB orientation and depression. METHODS: We performed a web-based survey of a national sample of US young adults aged 18-30 years. We assessed the respondents' LGB orientation, negative social media experiences, and depression using the 9-item Patient Health Questionnaire. We used generalized structural equation modeling to assess both the direct and indirect effects (via negative social media experiences) of LGB orientation on depression while controlling for relevant demographic and personal characteristics. RESULTS: We found a conditional indirect effect (ab path) of LGB orientation on depressive symptoms via negative social media experience (a: observed coefficient 0.229; P<.001; bias-corrected bootstrapped 95% CI 0.162-0.319, and b: observed coefficient 2.158; P<.001; bias-corrected bootstrapped 95% CI 1.840-2.494). The results show that among LGB respondents, for those who reported negative social media experiences in the past year, a 1 unit increase in these experiences was associated with a 0.494 unit increase in depressive symptomatology. CONCLUSIONS: Our results suggest that higher rates of depression among LGB young adults are partially explained by negative social media experiences; these results could help inform future patient/provider conversations about mental health risk and protective factors related to social media use. Reducing these experiences and increasing positive social media experiences among LGB persons may mitigate depressive symptomatology in this 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".