Social media use, economic recession and income inequality in relation to trends in youth suicide in high-income countries: a time trends analysis
Bibliographic record
Abstract
BACKGROUND: Suicide rates have risen in young people in several high-income countries over the last decade. Reasons for the increases are unclear. METHODS: We analysed trends in suicide rates in 15-24 year olds over the period 2000-2017 in high-income countries with populations >20 million using Joinpoint analysis. We investigated differences in the following population-level exposures between countries that are and are not experiencing suicide rates rises: 1) 2008 economic recession as indexed by changes in GDP; 2) Gini income inequality; 3) daily social media use. RESULTS: Four of the 11 countries studied are experiencing youth suicide rate rises: Australia, Canada, the UK, the USA. The year the increase began ranged from 2003 (95% confidence interval: 2002, 2007) in the UK to 2009 (95% CI: 2007, 2012) in Australia. There was little evidence of an association between social media use and youth suicide trends, and inconsistent evidence regarding the impact of the 2008 economic recession. Suicide rate rises were seen in countries with higher GDP per capita (Wilcoxon rank sum (WRS) z=-2.27; p=0.02) and income inequality (WRS z=-2.45; p=0.01) in 2008. LIMITATIONS: Suicide data were only available until 2016/2017. Social media and income inequality data were not available for all study years. The effect of other important factors were not investigated due to a lack of comparable data. CONCLUSIONS: Our analyses indicate that the most populous high-income countries experiencing a rise in youth suicide rates are predominantly English-speaking, with higher levels of income inequality and GDP. These findings provide preliminary evidence regarding possible contributory factors to guide further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".