Suicide attempts among Greenlandic forensic psychiatric patients – prevalence and determinants
Bibliographic record
Abstract
This nationwide cross-sectional study of the lifetime prevalence and determinants of suicide attempts includes 90% of Greenlandic forensic psychiatric patients. Retrospective data were collected from electronic patient files, court documents, and forensic psychiatric assessments using a coding form from a similar study. We used unpaired t-tests and chi2 or Fisher’s exact test. The lifetime prevalence of suicide attempts was 36% (n = 32), and no difference in prevalence was found between male and female patients (p = 0.95). Patients having attempted suicide had a higher rate of physical abuse in childhood (p = 0.04), family history of substance misuse (p = 0.007), and criminal convictions among family members (p = 0.03) than patients who had never attempted suicide. Women primarily used self-poisoning in their latest suicide attempts (67%), whereas men more often used sharp objects or a firearm (42%). Over a third of Greenlandic forensic patients have attempted suicide at some point in their life, and patients with traumatic childhood experiences are at higher risk of suicidal behaviour. It is not possible to conclude whether the lifetime prevalence of suicide attempts among Greenlandic forensic patients is comparable to that of other high-risk groups in other Arctic regions due to methodological differences among the very few other comparable studies.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".