Economic crises and incidence of suicide in Iceland 1911-2017
Bibliographic record
Abstract
INTRODUCTION: Suicides are number 16 as a cause of death worldwide. Causes are not always known, often associated with depression or trauma. Suicide incidence has decreased world- wide in the past three decades. The economic crisis of 2008 led to an increase in many countries. Many confounding factors make comparisons between countries difficult. This study assesses the possible impact of economic crises in Iceland on suicide incidence. MATERIAL AND METHODS: The work is based on suicide data from 1911 to 2017 and six economic crises from 1918 to 2008. The incidence is calculated five and ten years before and after the index year of each crisis. Possible crisis impact was assessed by applying a quasi-Poisson model to the data. Variance can be greater than model shows, so overdispersion was assessed. The evolution over time is assessed by inspection of cumulative sum of squared -residuals (CUSUMSQ). RESULTS: Suicide incidence increased from 1930, beginning to decline around 1990. Given a small population size there are wide upwards incidence fluctuations, within and outside the crisis -periods. The crises of 1931 and 1948 showed an increase, wheras in the others there is no change or a decrease. The sizes of deviations from expected value are, for the whole period, in compliance with the quasi-poisson model for counts. CONCLUSION: There is no statistical correlation between the six economic crises and suicide incidence in the Icelandic data. The study is based on population incidence and does not preclude a negative impact of economic crises on individuals.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".