Implications of addiction diagnosis and addiction beliefs for public stigma: A cross‐national experimental study
Bibliographic record
Abstract
INTRODUCTION: Stigmatisation of alcohol and other drug (AOD) use disorders poses a significant barrier to treatment access. A review by the World Health Organization concluded that addictive disorders were the most stigmatised health condition. Few studies have examined whether different etiological models of addiction (MOA) have implications for public stigma toward AOD disorders. The current study examined whether beliefs representative of five MOA predict public stigma levels and whether stigma differs for AOD use disorders relative to other health conditions. METHODS: Survey data were collected from Canada, the USA and Australia using an online data collection platform. Participants were randomised to one of four vignette manipulations describing an individual with an alcohol use disorder and/or other disorder. Participants' stigma toward the vignette character and beliefs related to five MOA (disease, moral, psychological, sociological, nature) were measured. RESULTS: Stigma ratings were significantly higher in the alcohol use disorder condition compared to other conditions. Two MOA accounted for significant variance in stigma ratings, where greater beliefs in the nature and psychological MOA predicted significantly lower levels of stigma toward alcohol use disorder. Contrary to predictions, beliefs in the disease MOA did not relate to lower stigma. Lastly, beliefs in the moral MOA partly accounted for geographical region differences (the USA vs. Canada) in public stigma. DISCUSSION AND CONCLUSIONS: The current study provides further experimental support that AOD disorders are more stigmatised than others. Additionally, the findings suggest that MOA may relate differentially to perceived stigma, and that regional variability in such beliefs exists.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".