Correction: Time trends and geographical patterns in suicide among Greenland Inuit
Bibliographic record
Abstract
Background Between 1980 and2018 Greenland has had one of the highest suicide rates in the world with an average rate of 96 suicides per 100,000 people annually.The aim of this study is to investigate suicide rates in Greenland according to age, birth cohort, period, sex, place of residence and suicide method from 1970 until 2018.Methods Suicide rates were examined using register and census data from 1970-2018 among Greenland Inuit.Rates were calculated by Poisson regression in Stata and by use of Excel.In analyses of the period trends, rates were standardized according to the World Standard Population 2000-2025. ResultsThe suicide rate has been declining since a peak at 120 suicides per 100,000 people annually in the 1980s but remained high at a rate of 81.3 suicides per 100,000 people annually from 2015-2018.Descriptive analyses point to the decrease in male suicides as the primary factor for the overall decreasing rates while the rate among women has been increasing.Simultaneously, the proportion of women who used a violent suicide method increased from 60% in 1970-1979 to 90% in 2010-2018.The highest rates are seen among young people, especially young men aged 20-24 years and youth suicide rates increased with later birth cohorts.When the rates started to increase in the 1980s both the capital Nuuk and East Greenland had the highest rates.Since then, the rate in Nuuk has declined while the rate in East Greenland was three times the national rate from 2015-2018.Conclusions From 1970 to 1989 the suicide rate increased from 28.7 to 120.5 per 100,000 people mirroring a rapid societal transition in the post-colonial period.The rate has slowly declined from the peak in the 1980s but remains at a very high level.Young people in general are at risk, but the steady increase in the rate among women is worrying and there is a need to investigate underlying causes for this development.
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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 | 0.006 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.091 | 0.041 |
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".