Remoteness and its impact on the potential for mental health initiatives in criminal courts in Nunavut, Canada
Bibliographic record
Abstract
Remoteness in the isolated communities of Nunavut, Canada adversely affects access to mental health services. Mental health initiatives in criminal courts exist in many cities to offer healthcare alternatives to regular criminal court processing for people affected by mental illness. These initiatives do not exist in Nunavut. A qualitative multiple-case study in 3 Nunavut communities involving 55 semi-structured interviews and 3 focus groups explored perceptions by health, justice and community stakeholders of the potential for criminal court mental health initiatives in the territory. Findings suggest remoteness is perceived to hinder mental healthcare support for court responses to people affected by mental illness, creating delay in psychiatric assessments and treatment. While communication technologies, such as tele-mental health, are considered an effective solution by most health professionals, many justice-sector participants are sceptical because of perceived limits to accessibility, reliability and therapeutic value. These perceptions suggest remoteness is a significant hurdle facing future criminal court mental health initiatives in Nunavut. Additionally, remoteness is viewed as affecting decisions by lawyers to bypass legislated mental health avenues, possibly resulting in more people with mental illness facing criminal justice sanctions without assessment and treatment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".