Building New Approaches to Risk Reduction With Social Networks and People Who Smoke Illegal Drugs From Participatory Community-Based Research
Bibliographic record
Abstract
Background: Global cases of overdose-related deaths attributed to synthetic opioids are reaching epidemic proportion in many jurisdictions. While the main focus of health agencies and the different levels of government has been to combat the cases linked to injection drug use, the deaths attributed to smoking illegal drugs have not gained the same attention. Moreover, little attention has been given to the role of people with past or current experiences of illegal drug use and how their social networks can mitigate the risk of a highly stigmatized behavior such as smoking illegal drugs. Methods: The study concerns the first social network research conducted via a community-based participatory action methodology in two distinct urban (Vancouver) and rural (Abbotsford) centers in British Columbia, Canada. The study will identify the influence of social networks on people who smoke illegal drugs (PWSID) and their adherence to interventions aimed at reducing harm. Through community consultations, members of the Vancouver Area Network of Drug Users and the British Columbia/Yukon Association of Drug War Survivors not only assisted with the design of this research project but also assisted with the data collection, management, protection and entry of demographic, and network information. Discussion: Many traditional qualitative and quantitative methods have not effectively engaged people who use drugs as researchers or collaborators due to stigma related to illegal drug use. The aim of this study is to recognize that everyone within the network of PWSID is a few steps away from harm. Therefore, we aim to reduce the harm associated with smoking of illegal drugs, especially for PWSID that are at the highest risk. At the same time, we hope that the social network research via a participatory community-based approach will mobilize PWSID in the process and offer a different method of knowledge construction from the traditional positivist approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".