Stigma Against Patients With Substance Use Disorders Among Health Care Professionals and Trainees and Stigma-Reducing Interventions: A Systematic Review
Bibliographic record
Abstract
PURPOSE: In this systematic review, the authors examine the prevalence and extent of stigmatizing attitudes among health care professionals (HCPs) and trainees against patients with substance use disorders (SUDs), including research on interventions to reduce stigma. METHOD: The authors searched 7 databases for articles published from January 1, 2011, through February 15, 2023, that quantified SUD stigma among HCPs or trainees. Inclusion criteria allowed both observational and intervention studies from the United States or Canada to be included in this review. Quality assessment was applied to all included studies; studies were not excluded based on quality. RESULTS: A total of 1,992 unique articles were identified of which 32 articles (17 observational studies and 15 intervention studies), all conducted in the United States, met the inclusion criteria. Half of the included studies (16 of 32) were published in 2020 or later. Most of the intervention studies (13 of 15) used a single-group pre-post design; interventions involved didactics and/or interactions with persons with SUDs. The 32 included studies used a total of 19 different measures of stigma. All 17 observational studies showed some degree of HCP or trainee stigma against patients with SUDs. Most intervention studies (12 of 15) found small but statistically significant reductions in stigma after intervention. CONCLUSIONS: SUD stigma exists among HCPs and trainees. Some interventions to reduce this stigma had positive impacts, but future studies with larger, diverse participants and comparison groups are needed. Heterogeneity among studies and stigma measures limits the ability to interpret results across studies. Future rigorous research is needed to determine validated, consensus measures of SUD stigma among HCPs and trainees, identify stigma scores that are associated with clinical outcomes, and develop effective antistigma interventions for HCPs and trainees.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".