A comparison between the age patterns and rates of suicide in the Islamic Republic of Iran and Australia
Bibliographic record
Abstract
BACKGROUND: When planning interventions aimed at preventing suicide, it is important to consider how socioeconomic and cultural factors may affect suicide rates. There has been variability in the accuracy of recording suicide deaths, leading to varying levels of underestimation. Social, cultural and religious elements affect whether deaths resulting from suicide are reported as such and those responsible for reporting a death may avoid providing information that would suggest the death was due to suicide. AIMS: The aim of this study was to document Iranian suicide patterns in 2006-2010 and 2011-2015, compare them with those in a "Western" country (Australia) and explore whether differences point to factors that affect suicide rates. METHODS: Data were obtained from Iranian and Australian national statistics offices. RESULTS: Peak Iranian male suicide rates were in young adulthood. There was a modest increase between the 2 quinquennials studied. Australian male rates were much higher, with age peaks in middle age and very late life. From age 30, the female rate was twice as high in Australia, graphs of the age patterns being relatively flat in both countries. Male:female ratios were 2.34 (Islamic Republic of Iran) and 3.25 (Australia). CONCLUSION: The suicide rate in the Islamic Republic of Iran is low, as in most other predominantly Muslim countries. Higher rates in youth are of concern. A case-control psychological autopsy study comparing cases in Iran and Australia could help answer questions about suicide causation.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".