Evaluating the Pragmatic and Moralistic Approach to Drug Policy and Addiction in Opioid Epidemic Outcomes
Bibliographic record
Abstract
Drug use, policy and outcomes differ in all countries; however, trends exist in response to these circumstances and can typically be evaluated through a pragmatic and moralistic lens. The public health, and evidence-based pragmatic approach differs from the law enforcement-centered moralistic approach, specifically in outcomes of people suffering from substance use disorder. Particularly for opioid use disorder, countries that have taken the pragmatic approach in response to opioid epidemics have had dramatic results. Two of the countries discussed include Switzerland and Portugal, with additional information on the Netherlands. In contrast, current opioid epidemics exist in certain countries who maintain a moralistic approach - namely the United States, with additional information on Canada who is experiencing a parallel epidemic. Though evidence demonstrates a pragmatic approach to drug policy and addiction will faire positive outcomes, hesitance to implement public health prevention and harm reduction policies remains. This paper discusses the context, dynamic, and policy behind countries that were able to combat the opioid crisis, while comparing these lenses to countries that struggle to achieve similar results. Moreover, this paper includes recommendations for countries with rising opioid epidemics to expand pragmatism in their approach to drug policy and addiction to improve the health of their populations.
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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 | 0.207 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".