Stimulants and Cannabis Use Among a Marginalized Population in British Columbia, Canada
Bibliographic record
Abstract
High rates of substance use, especially cannabis and stimulant use, have been associated with homelessness, exposure to trauma, and involvement with the criminal justice system. This study explored differences in substance use (cannabis vs. stimulants) and associations with trauma and incarceration among a homeless population. Data were derived from the BC Health of the Homeless Study (BCHOHS), carried out in three cities in British Columbia, Canada. Measures included sociodemographic information, the Maudsley Addiction Profile (MAP), the Childhood Trauma Questionnaire (CTQ), and the Mini International Neuropsychiatric Interview (MINI) Plus. Stimulant users were more likely to be female (43%), using multiple substances (3.2), and engaging in survival sex (14%). Cannabis users had higher rates of lifetime psychotic disorders (32%). Among the incarcerated, cannabis users had been subjected to greater emotional neglect (p < .05) and one in two cannabis users had a history of lifetime depressive disorders (p < .05). Childhood physical abuse and Caucasian ethnicity were also associated with greater crack cocaine use. One explanation for the results is that a history of childhood abuse may lead to a developmental cascade of depressive symptoms and other psychopathology, increasing the chances of cannabis dependence and the development of psychosis.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".