Bibliographic record
Abstract
Abstract Religion, in both its personal and institutional forms, is a significant force influencing the health of populations across the life course. Decades of research have documented that expressions of faith and the practice of spiritual pursuits exhibit significantly protective effects for physical and mental health, psychological well-being, and population rates of morbidity, mortality, and disability. This finding has been observed across sociodemographic categories, across nations and cultures, across specific disease outcomes, and regardless of one’s religious affiliation. A salutary religious effect on health and well-being is especially apparent among older adults, but is also observed across generations and age cohorts. Moreover, this association has been persistently found for various religious indicators, including attendance at worship services, prayer and other private practices, subjective feelings of religiosity, and numerous measures of religious behaviors, attitudes, beliefs, and experiences. Finally, a protective or primary preventive effect of religion has been observed in clinical, epidemiologic, social, and behavioral studies, regardless of research design or methodology. Faith-based organizations also have contributed to the health of populations, in partnerships or alliances with medical institutions and public health agencies, many of these dating back many decades. Examples include congregational health promotion and disease prevention programs and community-wide interventions, especially targeting the health and well-being of older congregants and those in less well-resourced communities, as well as faith–health partnerships in healthcare delivery, public health policymaking, and legislative advocacy for healthcare reform. Religious denominations and institutions also play a substantial role in global health development throughout the world, individually and in partnership with national health ministries, transnational medical mission organizations, and established nongovernmental agencies. These efforts focus on a wide range of goals and objectives, including building public health infrastructure, addressing ongoing environmental health needs, and responding to acute public health challenges and crises, such as infectious disease outbreaks. Constituencies include at-risk populations and cohorts throughout the life course, and programming ranges from perinatal care to maternal and child healthcare to geriatric medicine.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".