Substance Dependence Among Bipolar, Unipolar Depression and Psychotic Homeless: A Canadian National Study
Bibliographic record
Abstract
Introduction: Homeless individuals are often mischaracterized as members of a homogeneous population that suffers from a wide mental health and addiction issues, with little consideration of potentially important differences within or between samples. The aim of the present study was to investigate the comorbidy of alcohol and/or substance dependence (ASD) and major psychiatric diagnoses (bipolar disorder, unipolar depression and psychotic disorder) in a large Canadian sample of homeless individuals, and to examine potential sources of variability including location and ethnicity. Materials and Methods: A sample of 1585 homeless individuals were assessed for alcohol and/or substance dependence and bipolar disorder, unipolar depression and psychotic disorder with the Mini-International Neuropsychiatric Interview (version 6.0). Regional and ethnic differences in major psychiatric diagnoses between homeless with and without ASD were examined using univariate (i.e., chi-square) and multivariate (i.e., logistic regression) statistics. Results: Members of the sample with ASD were found to be younger, Aboriginal, less well educated, and born in the Americas. They were more significantly more prevalent in Western Canada and less prevalent in Central and Eastern Canada. The odds of having ASD were higher among people affected by bipolar disorder and (to a less extent) unipolar depression. Limitations: Data collected were self-reported and no urinalyses were performed. We considered diagnosis of ASD according to the previous 12 months only. Conclusions: Homeless people with major mental illness are at high risk for concurrent ASD, however the prevalence of ASD varies significantly between cities, and based on ethnicity and specific psychiatric diagnosis (with greater prevalence in individuals affected by bipolar disorder and, to a less extent, unipolar depression). Clinicians, administrators and policy makers should develop and deliver services based on careful assessment of the local population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".