A major stakeholder in the lobbying efforts: the role of people who use drugs in the past and the current opioid crisis
Bibliographic record
Abstract
Mass media campaigns have been effectively used in the past to shape public opinion on health. As the synthetic opioid epidemic sadly continues to take the lives of people across the globe, there is little information available on the power of mass media influenced by the advocacy and engagement of people who use drugs (PWUDs). Using one of the oldest and potentially most significant drug user advocacy groups in North America, the Vancouver Area Network of Drug Users (VANDU), as a case study, this study reports on the relationship between overdose-related deaths and the level of community action taken by PWUDs. The level of community action is measured annually via news items attributed to VANDU members or the organization's engagement with mass media, as well as overdose death numbers and rates. SPSS version 30 is used for analyzing correlation and simple regression. After conducting Kendall’s correlation, the results indicate a weak but statistically significant relationship between community action and the level of overdose deaths (r = − 0.023; p < 0.001) and overdose death rates (r = − 0.034; p < 0.001). Additionally, a simple linear regression model was estimated. Based on the regression equation, there was a significant effect between the community action level (frequency of news items shaped by VANDU membership), rate and number of deaths, where increasing drug overdose death numbers (F (1, 25) = 6.096, p < 0.021, R 2 = 0.196) and rates (F (1, 25) = 6.467, p < 0.018, R 2 = 0.206) predicted the level of community action by PWUDs. PWUDs’ role as significant stakeholders is often overlooked; however, this research has highlighted that the PWUDs have the capacity and potential to influence, shape, and change public opinion through engagement with news media. Therefore, government, cities, health agencies, and policymakers must engage with PWUDs and establish similar organizations, such as VANDU, to promote harm reduction, human rights, and treatment during an overdose crisis to facilitate shaping public opinion around addiction as a relapsing medical issue.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".