Bibliographic record
Abstract
Summary Harm reduction can be thought of as any program or policy designed to reduce drug-related harm without requiring the cessation of drug use. An example of a successful harm reduction strategy is the use of needle exchange programs or syringe exchange programs. Unfortunately, decisions may be made by policy makers that conflict with scientific literature and the abundance of evidence supporting such harm reduction measures. Evidence in favour of harm reduction strategies is robust, and their implementation is required in any comprehensive public health policy attempting to improve the well-being of society as a whole. This article examines the evidence supporting the efficacy of one such harm reduction strategy, namely, needle exchange programs; it also outlines a simple approach for medical students, residents, physicians, and other health care professionals to be involved in change on a global scale by discussing the Vienna Declaration as a tool for influencing evidence-based drug policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.404 | 0.672 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.035 | 0.050 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.053 | 0.048 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".