Stigma and substance use disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: To collect and update published information on the stigma associated with substance abuse in nonclinical samples, which has not been recently reviewed. RECENT FINDINGS: Searching large databases, a total of only 17 articles were published since 1999, with the majority of studies conducted outside the United States. Using major stigma concepts from a sociological framework (stereotyping, devaluation in terms of status loss, discrimination, and negative emotional reactions), the studies reviewed predominantly indicated that the public holds very stigmatized views toward individuals with substance use disorders (SUDs), and that the level of stigma was higher toward individuals with SUDs than toward those with other psychiatric disorders. SUMMARY: The prevalence of SUDs is increasing in the US general population, but these disorders remain seriously undertreated. Stigma can reduce willingness of policymakers to allocate resources, reduce willingness of providers in nonspecialty settings to screen for and address substance abuse problems, and may limit willingness of individuals with such problems to seek treatment. All of these factors may help explain why so few individuals with SUDs receive treatment. Public education that reduces stigma and provides information about treatment is needed.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".