Substance use by social workers and implications for professional regulation
Bibliographic record
Abstract
Abstract Purpose The purpose of this paper is to explore the prevalence and patterns of substance use among Canadian social workers. With legalisation of can professional regulatory bodies are pressed to consider implications of substance use for their members. Design/methodology/approach An online survey collected data about demographics and substance use prevalence and patterns. Statistical analysis involved pairwise comparisons, binary logistic regression models and logistic regression models to explore correlations between substance use and demographic and work-related variables. Findings Among the respondents (n=489), findings indicate that past-year use of cannabis (24.1 per cent), cocaine (4.5 per cent), ecstasy (1.4 per cent), amphetamines (4.3 per cent), hallucinogens (2.4 per cent), opioid pain relievers (21.0 per cent) and alcohol (83.1 per cent) are higher than the general Canadian population. Years of work experience and working night shift were significant predictors of total number of substances used in the past year. Use of a substance by a person when they were a student was highly correlated with use when they were a professional. Research limitations/implications Prevalence of substance use among social workers was found to be higher than the Canadian population; potential due to the anonymous nature of data collection. Originality/value This study has implications for social conceptualisations of professionalism and for decisions regarding professional regulation. Previous literature about substance use by professionals has focussed predominantly on implications for increased surveillance, monitoring, and disciplinary action. We contend that since substance use among professionals tends to be concealed, there may be exacerbated social misconceptions about degree of risk and when it is appropriate to intervene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".