Increased suicide risk among younger women in winter during full moon in northern Europe. An artifact or a novel finding?
Bibliographic record
Abstract
Available evidence suggests that there is no effect of moon phases on suicidal behavior. However, a Finnish study recently reported elevated suicide rates during full-moon, but only among premenopausal women and only in winter. This could not be replicated in an Austrian study and stirred a discussion about whether the Finnish finding was false-positive or if there are unaccounted moderator variables differing between Finland and Austria. The goal of the present study was to provide another replication with data from Sweden, which is geographically more comparable to Finland than Austria. We also investigated the discussed moderator variables latitude and nightly artificial brightness. There were 48,537 suicides available for analysis. The fraction of suicides during the full-moon quarter in winter did not differ significantly from the expected 25% among premenopausal women (23.3%) and in the full sample (24.7%). The incidence risk ratios for full moon quarter in Poisson regression models were 0.96 (95% CI: 0.90-1.02) for premenopausal women and 1.01 (95% CI: 0.99-1.04) for the full sample. According to Bayes-factor analysis, the evidence supports the null-hypothesis (no association) over the alternative hypothesis (some association). We found similar results when we split the data by latitude and artificial nightly brightness, respectively. In line with the Austrian study, there was no increase of suicides in Sweden among premenopausal women in winter during full-moon. The results from the Finnish study are likely false positive, perhaps resulting from problematic but common research and publication practices, which we discuss.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".