Substance use by social workers and implications for professional regulation
Bibliographic record
Abstract
Purpose. This study explores the prevalence and patterns of substance use among Canadian social workers. Legalisation of cannabis is forthcoming in Canada in 2018 and it is anticipated that professional regulatory bodies will be pressed to consider implications for their members. Methodology. 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 n=489 respondents, findings indicate that past-year use of cannabis (24.1%), cocaine (4.5%), ecstasy (1.4%), amphetamines (4.3%), hallucinogens (2.4%), opioid pain relievers (21.0%), and alcohol (83.1%) 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. Discussion. 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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".