Suicide rates in South Korea and internationally following release of the Netflix series ‘Squid Game’
Bibliographic record
Abstract
Widely disseminated media depictions of suicide can result in increased suicides, the so-called Werther effect . Season one of the Netflix series Squid Game (SG) was released in September 2021 and contained multiple depictions of suicide, but suicide was not an obvious theme of the show. This study sought to identify whether release of SG resulted in a Werther effect . We examined changes in suicide rates in the three-month period following release of SG including weekly suicide rates from the country where SG was filmed (South Korea) (2013-2022) and monthly rates from nine comparator countries (Japan, Taiwan, Germany, Spain, UK, USA, Colombia, Mexico, Türkiye) (2015-2022). We conducted interrupted time series (ITS) analyses using quasi-Poisson regression, adjusting for impact of the COVID-19 pandemic, linear trends, and seasonality. There was no evidence of a change in suicides in South Korea in the four weeks after release of SG (IRRs: 0.86, 95% CI 0.71-1.05; 1.13, 95% CI 0.95-1.34; 0.85, 95% CI 0.70-1.02; and 1.08, 95% CI 0.90-1.28, respectively). Age- and sex-stratified results likewise indicated no change in suicide in any specific demographic group. There was no change in monthly suicide rates in the 3-month period following SG in eight of 10 countries with an increase observed in Germany (IRR 1.12, 95% CI 1.03-1.22) and a decrease observed in the UK (IRR 0.88, 95% CI 0.80-0.97). These findings indicate that SG did not produce Werther effects. Further studies are needed to confirm if this finding generally applies to entertainment media where suicides are included but not a major theme. • Suicides can increase following media portrayals of suicide (the “Werther Effect”) • We aimed to assess suicides following release of the Netflix series ‘Squid Game’ • We examined changes in suicide rates in South Korea and nine other countries • Our findings indicate that ‘Squid Game’ did not produce Werther effects • More research is needed on the impact of nuanced, fictional depictions of 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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".