Bibliographic record
Abstract
Objective: To estimate the associations between measures of religiousness and depression\nand to determine if these associations have changed over the period 1952 to 1992.\nMethods: Data were drawn from 2,398 individuals from the 1952 and 1992 cross\nsectional surveys of the Stirling County Study as a means of studying time trends. For this\nthesis, questions about frequency of religious worship attendance, frequency of saying\ngrace, religious importance were employed to develop a scale of secularism. The individual\nquestions and the scale were analyzed in terms of the prevalence of depression at each time\npoint. Logistic regression was used to determine associations of depression with religion\nvariables, adjusted for demographic and other covariates.\nResults: Individuals who attended religious services weekly were over two times less\nlikely to meet criteria for depression than infrequent attenders and this relationship did not\nchange over time. Associations between religious attendance and depression were stronger\namong women and the medically healthy compared to men and those with a medical\ncondition. Being more secular was associated with higher odds of depression among\nfemales.\nConclusions: Religious attendance has consistently been associated with lower\ndepression over a forty year period, irrespective of marked declines in population-level\nreligious behaviors. Associations between religiousness and depression may be stronger in\nfemales than in males.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".