Association of holidays and the day of the week with suicide risk: multicountry, two stage, time series study
Bibliographic record
Abstract
OBJECTIVES: To assess the short term temporal variations in suicide risk related to the day of the week and national holidays in multiple countries. DESIGN: Multicountry, two stage, time series design. SETTING: Data from 740 locations in 26 countries and territories, with overlapping periods between 1971 and 2019, collected from the Multi-city Multi-country Collaborative Research Network database. PARTICIPANTS: All suicides were registered in these locations during the study period (overall 1 701 286 cases). MAIN OUTCOME MEASURES: Daily suicide mortality. RESULTS: Mondays had peak suicide risk during weekdays (Monday-Friday) across all countries, with relative risks (reference: Wednesday) ranging from 1.02 (95% confidence interval (CI) 0.95 to 1.10) in Costa Rica to 1.17 (1.09 to 1.25) in Chile. Suicide risks were lowest on Saturdays or Sundays in many countries in North America, Asia, and Europe. However, the risk increased during weekends in South and Central American countries, Finland, and South Africa. Additionally, evidence suggested strong increases in suicide risk on New Year's day in most countries with relative risks ranging from 0.93 (95% CI 0.75 to 1.14) in Japan to 1.93 (1.31 to 2.85) in Chile, whereas the evidence on Christmas day was weak. Suicide risk was associated with a weak decrease on other national holidays, except for Central and South American countries, where the risk generally increased one or two days after these holidays. CONCLUSIONS: Suicide risk was highest on Mondays and increased on New Year's day in most countries. However, the risk of suicide on weekends and Christmas varied by country and territory. The results of this study can help to better understand the short term variations in suicide risks and define suicide prevention action plans and awareness campaigns.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".