Comfort in Providing Care and Associations With Attitudes Towards Substance Use: A Survey of Mental Health Clinicians at an Urban Hospital in Vancouver, Canada
Bibliographic record
Abstract
INTRODUCTION: Stigma is a major driver of harms associated with substance use and can interfere with the provision of high-quality, effective healthcare for people who use drugs. Our study aimed to explore the relationship between mental health clinicians' comfort in providing substance use care and their attitudes towards substance use. METHODS: In this cross-sectional study, the Brief Substance Abuse Attitudes Survey was administered among a convenience sample of mental health clinicians [N = 71] working in an acute care setting in Vancouver, Canada. One-way ANOVA and the Kruskal-Wallis test were used to examine the association between three levels of comfort and five predefined attitude subgroups. STROBE checklist for cross-sectional studies was used. RESULTS: Level of comfort was significantly associated with attitudes towards substance use across three subscales: permissiveness, nonstereotyping and treatment optimism. In pairwise comparisons, the neutral group held significantly less permissive attitudes when compared to the comfortable group. However, the neutral group held more stereotypical views and less optimism about treatment outcomes, when compared to the comfortable and uncomfortable groups, respectively. DISCUSSION AND CONCLUSIONS: Our findings highlight that mental health clinicians who are undecided or neutral about their comfort in providing substance use care are more likely to have negative views towards people with substance use disorders. Future work should explore, implement and evaluate education and training to reduce substance use disorder-related stigma among mental health clinicians and other health professionals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".