Impact of migration from an illicit drug scene on hospital outcomes among people who use illicit drugs in Vancouver, Canada
Bibliographic record
Abstract
INTRODUCTION AND AIMS: People who use illicit drugs (PWUD) are vulnerable to an array of negative health outcomes, and increased hospital services utilisation. PWUD are also a transient population which poses challenges to the provision of optimal health care. The objective of this study was to identify out-migration patterns from Vancouver's Downtown Eastside (DTES), a neighbourhood where services for PWUD are concentrated, and to estimate the impact of these patterns on hospitalisation events among PWUD. DESIGN AND METHODS: Data were collected through three prospective cohorts of PWUD in Vancouver, which were linked with health administrative data. Latent class growth analysis was used to define migration trajectory groups. Poisson regression was used to estimate the effect of migration patterns on hospitalisation events. RESULTS: A total of 1180 participants were included in the study. Four latent classes were identified: early migration out (243, 20.6%); frequent revisit (112, 9.5%); late migration out (219, 18.6%); and consistently living in the DTES (606, 51.4%). Compared with those who consistently lived in the DTES, participants in the early migration out group had lower hospitalisation events (adjusted rate ratio = 0.65; 95% confidence interval: 0.48-0.90). DISCUSSION AND CONCLUSION: We found that PWUD who migrated out of the DTES early had lower hospitalisation events compared to those who consistently lived in the DTES, which may be a function of lesser addiction severity among this trajectory group. These findings underscore a need to provide transitional health and social service supports for other trajectory groups in an effort to minimise hospitalisation for preventable causes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".