EXPLORING THE RELATIONSHIP BETWEEN RELIGIOUS SERVICE ATTENDANCE, MENTAL DISORDERS, AND SUICIDALITY AMONG DIFFERENT ETHNIC GROUPS: RESULTS FROM A NATIONALLY REPRESENTATIVE SURVEY
Bibliographic record
Abstract
BACKGROUND: To date, sufficient data have not been available to examine ethnic differences in religiosity and mental health in the general population. However, evidence exists to suggest that the protective effects of religion may differ across ethnic groups. This study examined the relationship between religious attendance and mental health across ethnic groups. METHODS: The Collaborative Psychiatric Epidemiologic Survey (N = 20,130) is a large, ethnically diverse sample of adult, US respondents. Frequency of attendance at religious services was measured as: at least once per week (reference group), one to three times per month, less than once per month, or less than once per year. Multiple logistic regression analyses examined associations between religious attendance and mood, anxiety and substance use disorders, as well as suicidal ideation and attempts. Models adjusted for sociodemographics and comorbidity. RESULTS: Results differed when performed within each ethnicity. Infrequent religious attendance was associated with substance use disorders in Whites and Africans only (Adjusted Odds Ratio (AOR) = 2.30 [95% CI = 1.77-2.99]; AOR = 1.86 [1.25-2.79], respectively), and with anxiety and suicidal ideation in Whites (AOR = 1.44 [1.10-1.88]; AOR = 1.58 [1.24-2.01]) and Hispanics only (AOR = 2.35 [1.17-4.73]; AOR = 1.70 [1.15-2.52]). Asians were the only group in which religious attendance was associated with mood disorders (AOR = 4.90 [1.54-15.60]). Interaction terms were nonsignificant. CONCLUSIONS: The present study suggests that ethnicity is an important variable to consider in the relationship between religiosity and mental health. Future studies should attempt to either adjust for or stratify by ethnicity when examining these relationships.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".