Moving into an urban drug scene among people who use drugs in Vancouver, Canada: Latent class growth analysis
Bibliographic record
Abstract
BACKGROUND: Urban drug scenes are characterized by high prevalence of illicit drug dealing and use, violence and poverty, much of which is driven by the criminalization of people who use illicit drugs (PWUD) and the associated stigma. Despite significant public health needs, little is understood about patterns of moving into urban drug scenes among PWUD. Therefore, we sought to identify trajectories of residential mobility (hereafter 'mobility') among PWUD into the Downtown Eastside (DTES), an urban neighbourhood with an open drug scene in Vancouver, Canada, as well as characterize distinct trajectory groups among PWUD. METHODS: Data were derived from three prospective cohort studies of community-recruited PWUD in Vancouver between 2005 and 2016. We used latent class growth analysis (LCGA) to identify distinct patterns of moving into the DTES among participants residing outside of DTES at baseline. Multivariable multinomial logistic regression was used to determine baseline factors associated with each trajectory group. RESULTS: In total, 906 eligible participants (30.9% females) provided 9,317 observations. The LCGA assigned four trajectories: consistently living outside of DTES (52.8%); early move into DTES (11.9%); gradual move into DTES (19.5%); and move in then out (15.8%). Younger PWUD, those of Indigenous ancestry, those who were homeless or living in a single-room occupancy hotel (SRO), and those injecting drugs daily were more likely to move in then out of DTES (all p<0.05). Living in an SRO, daily injection drug use, and recent incarceration were also positively associated with early mobility (all p<0.05). CONCLUSIONS: Nearly half of the participants moved into the DTES. Younger PWUD and Indigenous peoples appeared to have particularly high mobility, as did those with markers of social-structural vulnerability and high intensity drug use. These findings indicate a need to tailor existing social and health services within the DTES and expand affordable housing options outside the DTES.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".