[Religion and suicide - part 2: confessions, religiousness, secularisation and national suicide rates].
Bibliographic record
Abstract
OBJECTIVE: National suicide rates differ remarkably. The influence of religion on the frequency of suicides was already stressed by Durkheim, however, character and dimension of this influence are still unclear. Our study claims to assess the association between (a) the distribution of believers of different religions, (b) the secularization, (c) the religiousness and the national suicide rates by gender. METHOD: Data of the distribution of religious confessions and of the religiousness of the inhabitants of the single countries were correlated with the national suicide rates and illustrated by means of Scatter/Dot-Plots. RESULTS: Independent of gender, low suicide rates were found in Islamic countries. Buddhist countries showed high suicide rates in women, and countries with a high percentage of inhabitants without confession high suicide rates in men. Only catholic countries showed an association between secularisation and suicide rates. In countries with a high proportion of religious inhabitants we found low suicide rates. CONCLUSIONS: Although none of the World religions support the human right of suicide, the mosaic religions of resurrection refuse suicide more strictly than the Eastern religions of reincarnation. All in all our study supports the hypothesis that religiousness can be seen as a protective factor against suicide.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".