The substance use profiles of adults who sought mental health and addiction services through a centralized intake process in Nova Scotia (2020–2021)
Bibliographic record
Abstract
Background: Despite the increasing substance use in Canada, our understanding of how substance use varies based on the intersections of gender, ethnicity/race, and income sources among preclinical populations remains limited. Thus, this study aimed to investigate, among clients of mental health and addiction (MHA) intake in Nova Scotia: 1) the prevalence of substance use by gender, ethnicity, and income source; 2) the routes of substance administration; 3) factors associated with substance use. Understanding how gender, ethnicity, and income sources intersect to influence substance use patterns is essential for designing prevention and treatment strategies tailored to an individual's unique needs. Additionally, exploring the various routes of substance administration can provide insight into potential health risks, helping to inform harm reduction strategies. Methods: This cross-sectional study included 22,500 adults who contacted MHA central intake in Nova Scotia in 2020 and 2021. Clients were assessed for substance use, substance use frequency, route of substance administration, and mental and physical health problems. The prevalence of substance use was examined as a function of gender, ethnicity, and income source. Multinomial logistic regression was used to investigate factors associated with substance use. Results: Among the included MHA Intake clients, 36.1% reported daily substance use. The highest prevalence of daily substance use was identified among homeless (69.7%) and non-White men on social assistance/disability (60.9%). Also, non-White individuals on social assistance/disability were more likely to engage in frequent (aOR = 2.66, 95% =1.64, 4.30) and daily (aOR = 2.82, 95% CI: 2.08, 3.82) substance use compared to White individuals. Being young (aged 19-29), lack of access to private insurance, current/past mental illness, moderate/high suicide risk, and presence of two or more psychosocial stressors, were associated with occasional, frequent, and daily substance use alike. Conclusions: The high prevalence of daily substance use among MHA Intake service users in Nova Scotia highlights the need for prevention and treatment strategies to address individual and structural level factors contributing to daily substance use.
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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 | 0.002 | 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 | 0.001 |
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".