The Synthetic Opioid Epidemic: A Study Protocol to Determine Whether People Who Use Drugs Can Influence or Shape Public Opinion via Mass Media
Bibliographic record
Abstract
Abstract BackgroundWe are currently witnessing an ongoing drug overdose death epidemic in many nations linked to the distribution of illegally manufactured potent synthetic opioids. While many health policy makers and researchers have focused on the root causes and possible solutions to the current crisis, there has been little focus on the power of advocacy and community action by people who use drugs (PWUDs). Specifically, there has been no research on the role of PWUDs in engaging and influencing mass media opinion.MethodsBy relying on one of the longest and largest peer-run drug user advocacy groups in the world, the Vancouver Area Network of Drug Users (VANDU), newspaper articles, television reports, and magazines that VANDU or its members have been directly involved in will be identified via two data bases (the Canadian Newsstream & Google News). The news articles and videos related to the health of PWUDs and issues affecting PWUDs from 1997 to the end of 2020 will be analyzed qualitatively using Nvivo software.ResultsNot Applicable because it is a study protocol and data has not been collected and analyzed. ConclusionAs our communities are entering another phase of the drug overdose epidemic, acknowledging and partnering with PWUDs could play an integral part in advancing the goals of harm reduction, treatment, and human rights. Trial registrationNot applicable because it uses secondary data.
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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 | 0.060 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.024 |
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".