Bibliographic record
Abstract
The purpose of the present study was to examine the question of whether or not commitment to religious beliefs is associated with better mental health in typical community members. A household interview survey was conducted in a stratified, clustered sample of 3% of the adults that resided in a largely religious and rural mountain community. The Duke Health Profile was used to assess mental and physical health, and the nature and depth of religious devotion or commitment was based on the response to an interview item. Of the respondents in the sample, the mean age was 48.7 years, 55% were women, and the average annual family income was $14 300 (US). In a simple unadjusted analysis, religiosity was significantly correlated with physical health (the ill were more religious) and gender (women were more religious), but not with mental health, age, income, education level, or geographic mobility. Mental health was correlated with gender (women scored lower), physical health (the ill scored lower), and income (the wealthy scored higher); but the correlations with these variables were largely in the opposite direction than religiosity. When the correlation between religiosity and mental health were adjusted for the economic, health, and demographic characteristics with the multiple partial correlation method, a definite correlation was found (r = 0.11 to 0.14, p = 0.005 to 0.032). The conclusion is drawn that there is an association between religious commitment and good mental health, but that it can be masked by the inverse dependencies of religion and mental health on economic, health and demographic factors. These results suggest that further investigation should be undertaken in order to elucidate the clinical utility of incorporating religious beliefs and practice into patient therapy.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".