Religiosity and health: A global comparative study
Bibliographic record
Abstract
The objective of this paper is to understand global connections between indicators of religiosity and health and how these differ cross-nationally. Data are from World Values Surveys (93 countries, N=121,770). Health is based on a self-assessed question about overall health. First, country-specific regressions are examined to determine the association separately in each country. Next, country-level variables and cross-level interactions are added to multilevel models to assess whether and how context affects health and religiosity slopes. Results indicate enormous variation in associations between religiosity and health across countries and religiosity indicators. Significant positive associations between all religiosity measures and health exist in only three countries (Georgia, South Africa, and USA); negative associations in only two (Slovenia and Tunisia). Macro-level variables explain some of this divergence. Greater participation in religious activity relates to better health in countries characterized as being religiously diverse. The importance in god and pondering life’s meaning is more likely associated with better health in countries with low levels of the Human Development Index. Pondering life’s meaning more likely associates with better health in countries that place more stringent restrictions on religious practice. Religiosity is less likely to be related to good health in communist and former communist countries of Asia and Eastern Europe. In conclusion, the association between religiosity and health is complex, being partly shaped by geopolitical and macro psychosocial contexts.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".