Forensic psychiatry in the arctic – a comparative study of patient characteristics, health care system and legislation in greenland and nunavut
Bibliographic record
Abstract
Introduction Greenland and the Canadian territory of Nunavut appear to have a different prevalence of forensic psychiatric patients, despite their comparable population and landmass sizes. Both are mainly inhabited by Inuit with a similar cultural and social background. Both have a universal health care system. They differ, however, concerning the supply of mental health services and legislation concerning forensic psychiatric patients. Objectives To compare the prevalence and clinical characteristics of forensic psychiatric patients in Greenland and Nunavut. Methods Data is obtained from health records, forensic psychiatric evaluations and court acts from all forensic psychiatric patients 18 years or older living in Greenland or admitted to the University Hospital Aarhus (N≈100). Data extracted from Nunavut Review Board hospital reports will be used to describe the patient population from Nunavut (N≈15). Patient characteristics include gender, age, marital status, education, diagnosis of mental illness, medical treatment, family history of mental illness and serious adverse childhood experiences. Public documents concerning health systems and legislation will be identified through literature search. Results Patient characteristics from the two patient populations, as well as visualizations of the differences and similarities between the respective health care and legislative systems will be presented at the conference. Conclusions This study provides a comprehensive clinical, socio-demographic and forensic comparison of the forensic psychiatric populations in Greenland and Nunavut, Canada. To our knowledge, it will be the first to describe and compare forensic psychiatric populations in the Arctic.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".