Authoritarianism, attitudes towardharm reduction and the stigmatisation of people who use drugs
Bibliographic record
Abstract
Introduction: While much research has been conducted in the past two decades on the stigma of mental health, relatively little has been published on the stigma of drug use and addiction, or attitudes toward harm reduction efforts.Although harm reduction is an approach particularly well suited for reducing the negative outcomes of drug use at a time when overdoses continue to escalate, negative opinions and anti-drug policies often interfere with the operation and funding of such programmes. Material and methods:A diverse group of students in the United States (N = 193) completed an online assessment of authoritarian beliefs, fa-Streszczenie Wprowadzenie: W ciągu ostatnich dwudziestu lat przeprowadzono wiele badań na temat stygmatyzacji osób z problemami zdrowia psychicznego, niewiele jednak na temat stygmatyzacji osób używających narkotyków i od nich uzależnionych oraz postaw wobec działań na rzecz redukcji szkód.Program redukcji szkód szczególnie dobrze służy zmniejszaniu negatywnych skutków używania narkotyków, podejście to jest ważne zwłaszcza w sytuacji stale zwiększającej się liczby osób, które przedawkowują narkotyki.Negatywne opinie i polityka antynarkotykowa często jednak nie sprzyjają realizowaniu i finansowaniu programów redukcji szkód.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".