“Something that actually works”: Cannabis use among young people in the context of street entrenchment
Bibliographic record
Abstract
BACKGROUND: Cannabis is one of the most widely used substances among vulnerable young people (<26 years of age) experiencing street entrenchment. Although previous research has documented the role cannabis can play in harm reduction, substance use and mental health treatment and pain management, this research has predominantly been quantitative and focused on adult drug-using populations. Little qualitative work has examined how young people who use drugs understand, experience, and engage with cannabis in the context of street entrenchment and drug use trajectories that include the use of other substances such as alcohol, opioids and crystal methamphetamine (meth). METHODS: Semi-structured, in-depth qualitative interviews were conducted between 2017 and 2019 with 56 young people recruited from a cohort of street-involved youth in Vancouver, Canada. We also conducted 13 interviews with 12 youth-focused care providers across the same time period. Interview data were triangulated by drawing on the findings of a program of anthropological research conducted by the senior author since 2008. Interviews were transcribed verbatim and thematic analysis was conducted. RESULTS: The vast majority of study participants engaged in daily, intensive cannabis use at the same time as they cycled on and off other substances that were perceived as much more harmful (primarily alcohol, fentanyl, heroin and meth). While most participants derived significant pleasure from the use of cannabis, no participants in our study described using cannabis for purely recreational purposes. A number of participants explicitly framed cannabis as a form of mental health and substance use treatment that was more effective and "healthier" than the long-term use of psychopharmaceuticals and medication-assisted substance use treatment (e.g., opioid agonist therapies). Cannabis use was also understood to ameliorate some of the harms of, or even facilitate transitions out of, periods of street-based homelessness. While the majority of our participants highlighted the positive effects of regular cannabis consumption, some described how intensive cannabis use could generate significant harms. CONCLUSION: In the context of the recent legalization of non-medical cannabis use in Canada and amid ongoing overdose and housing crises, it is imperative that future policy and programming interventions and provider education and training be responsive to the ways in which vulnerable youth in our setting are actively using cannabis to navigate their everyday lives and healthcare needs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".