Examining the relationship between public stigma, models of addiction, and addictive disorders
Bibliographic record
Abstract
Background: The stigmatization of addiction has been identified as a barrier to treatment-seeking among individuals with substance use concerns. Although some evidence exists that beliefs in different models of addiction (MOAs) are associated with stigma, the research is limited in several ways. The aim of the current study is to understand the relationship between different MOAs and public stigma toward substance use disorders and behavioural addictions. Method: Participants were 755 adults who completed an online survey on MTurk (Mage = 36.2, SD = 10.1, 40.3% women, 59.4% men) and were randomized to one of four vignette conditions describing an individual with alcohol use disorder, opioid use disorder, problem gambling disorder, or diabetes. Participants completed measures assessing perceived stigma towards the vignette character and beliefs related to five MOAs (disease, moral, psychological, sociological, nature). Results: Stigma ratings were significantly higher in the alcohol and opioid use disorder conditions compared to the problem gambling and diabetes conditions. Greater beliefs in the disease MOA were associated with greater stigma in the problem gambling condition, whereas greater beliefs in the moral MOA were associated with greater stigma in all addiction conditions. Greater beliefs in the psychological MOA were associated with lower stigma in the opioid use disorder and problem gambling conditions. Conclusions: The current study provides further support that addictive disorders are more stigmatized than other health disorders and suggests that beliefs in specific MOAs are differentially associated with stigma. Interventions addressing addiction stigma may consider incorporating information emphasizing MOAs that are less stigmatizing.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".