ASSOCIATION BETWEEN SOCIAL POLICY, STIGMA, SOCIAL ACCEPTANCE, AND MENTAL HEALTH AMONG OLDER MEN WHO HAVE SEX WITH MEN
Bibliographic record
Abstract
Abstract Background Older MSM (Men who have Sex with Men) often face discrimination and marginalization due to ageism and homophobia, impacting their psychological well-being. This study investigates the influence of social factors on the psychological well-being of older MSM, considering variations in social policy and cultural contexts across China, Hong Kong, and Taiwan. Methods A comparative approach was utilized, gathering data (N=453) from older MSM in the aforementioned regions. Measures assessing social policy, stigma, social acceptance, and psychological well-being were employed. Control variables, such as age, education level, and income, were included to mitigate potential influences. The data were analyzed using descriptive statistics, ANOVA, and regression analysis. Results The findings demonstrate significant negative effects of stigmatization across all three regions. F-statistics indicate the regression models’ overall significance: China (F = 69.583, p < 0.001), Hong Kong (F = 51.971, p < 0.001), and Taiwan (F = 57.725, p < 0.001). This highlights stigmatization’s strong influence on dependent variables in each region. Additionally, high adjusted R-squared values suggest substantial explained variance: China (Adj R2 = 0.632), Hong Kong (Adj R2 = 0.514), and Taiwan (Adj R2 = 0.565), emphasizing the model’s ability to account for variability in dependent variables. Conclusion The study underscores the need for targeted interventions and policy changes to combat stigma and promote social acceptance, thereby enhancing the psychological well-being of this vulnerable population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".