Substance Use and Mental Health among Canadian Social Workers
Bibliographic record
Abstract
This article reports the findings of an online survey designed to collect information about substance use (licit, illicit, or pharmaceutical) and mental health (depression or anxiety) among social workers. Among the 489 participants, Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) screenings indicated symptoms of depression and anxiety at a higher prevalence than those of the general Canadian population. There were relatively few correlations between mental health scores and substance use. PHQ-9 total score significantly predicted past-year antidepressant use and past-year sleeping medication use. GAD-7 total score significantly predicted past-year benzodiazepine use and past-year melatonin use. Effects of substances (e.g., cannabis, alcohol, benzodiazepines, cocaine, ecstasy) were predominantly beneficial or nonproblematic (e.g., enjoyment/pleasure; socializing enhanced; concentration/focus improved). Subjective experiences of social workers should be sought to understand potential relationships between mental health scores and enhancement effects of substance use. Substances are being used, at least in part, for their performance-enhancing effects to meet the expectations of day-to-day life. Interventions can shift toward root causes, with institutions held more accountable for supporting social workers and promoting "workplace care."
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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 | 0.002 | 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) | 0.004 | 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".