Religious Attendance, Spirituality, and Major Depression in Canada: A 14-Year Follow-up Study
Bibliographic record
Abstract
OBJECTIVE: Although there have been numerous studies on the relation of religion or spirituality and major depression, few used a longitudinal, nationally representative sample. Our study sought to examine the effect of religious attendance, self-declared importance of spiritual values, and self-identification as a spiritual person on major depression. METHOD: Data coming from 8 waves (1994 to 2008) of the longitudinal Canadian National Population Health Survey were used. People (n = 12 583) who were not depressed at baseline (1994) were followed during 14 years. Depression at each cycle was assessed using the Composite International Interview-Short Form for Major Depression. Weibull proportional hazards regression was used to model longitudinal risk of depression, with religious attendance or spirituality as a predictor. RESULTS: At baseline, monthly religious attenders tended to be older, female, and married, compared with occasional and nonattenders. The Weibull regression model revealed a 22% lower risk of depression for monthly attenders (hazard ratio 0.78, 95% CI 0.63 to 0.95), compared with nonattenders, after controlling for age, household income, family and personal history of depression, marital status, education, and perceived social support. Neither self-reported importance of spiritual values nor identification as a spiritual person was related to major depressive episodes. CONCLUSION: Attending religious services at least monthly has a protective effect against major depression.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".