Testing the religion/spirituality-mental health curvilinear hypothesis using data from many-analysts religion project
Bibliographic record
Abstract
Findings from the recent Many-Analysts Religion Project (MARP) have been characterized as supporting a robust positive relationship between various measures of religion and aspects of well-being. However different conceptualizations of religiosity (e.g. identity, attendance, belief, conviction) can theoretically be expected to display distinct (e.g. non-linear or curvilinear) patterns of relationships with different manifestations of well-being. Additionally extant analyses of MARP data have not addressed how influences such as social support affect the relationship between religion/spirituality (R/S) and well-being. The present analysis restricted to a subset of countries with the predominant religion of Christianity found that net demographic controls, meaning in life, and enjoyment of life was significantly higher among those identifying as religious, attending religious service, and identifying as believing in God. However, when God was modelled quadratically, both meaning in life and enjoyment of life demonstrated a “J-shaped” relationship, although the nuances for their interpretation were distinct. Thus, partial support was found for a quadradic or “J-shaped” relationship between religious belief and mental well-being. Finally, adjusting estimates for social support tended to diminish the importance of R/S variables for predicting well-being, suggesting that increased well-being evinces a complex relationship with religious belief.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".