Public Stigma Toward People With Drug Addiction: A Factorial Survey
Bibliographic record
Abstract
OBJECTIVE: Stigmatizing attitudes toward people with a drug addiction have detrimental effects on the lives of these people. However, the factors that influence stigma toward people with a drug addiction have not yet been thoroughly investigated, compared with the stigma of other mental illnesses. Based on attribution theory, our experiment examined to what extent individual and contextual characteristics of people with a drug addiction influence stigmatizing attitudes toward people with a drug addiction. Moreover, we explored whether respondent characteristics indicative of familiarity with addiction decrease stigma toward people with a drug addiction. METHOD: -design) that featured a fictional person with an addiction. Stigmatizing beliefs, such as blame or fear, were assessed using the Attribution Questionnaire (AQ-9). RESULTS: Different attributes of people with a drug addiction and of the characteristics of their addiction modulated stigma in ways that are mostly consistent with attribution theory and related research. For example, female gender and younger age of people with a drug addiction diminished several stigmatizing attitudes; greater duration of addiction and social influence to use drugs increased them. Furthermore, characteristics of respondents modulated stigma: women, younger respondents, and those with higher education expressed less-stigmatizing responses than others. CONCLUSIONS: The stigmatization of people with a drug addiction is influenced by several factors, including characteristics of the stigmatized person, the addiction, and the person holding stigmatizing attitudes. A better understanding of the underlying mechanisms of these effects is needed to develop evidence-based antistigma measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".