Bibliographic record
Abstract
Illicit opioid overdose deaths in British Columbia have increased five folds since 2012. Previous studies have identified potential factors that may affect the distribution of fatal and nonfatal overdose risks, such as socioeconomic status, stigma, and access to health care services. Most of these factors affect rural and urban areas differently, and I hypothesize that fatal/nonfatal overdose risks could be higher in rural areas than in urban areas because rural communities are often disadvantaged concerning these factors. The presence of rural-urban differences in overdose risks was confirmed by modelling the recurrent overdose rate and odds of fatal overdose per event for British Columbians who had at least one overdose between Jan 2015 and Dec 2018 with Poisson and logistic regression methods. Spatial variations in these two measures were then estimated using Generalized Additive Models; the results are mapped to identify communities and regions with the highest risk of fatal overdose. Long-term survival after a first overdose event was also investigated using Cox proportional hazard models under a multi-state framework that conceptualized overdose risk relative to Opioid Agonist Therapy (OAT) event history. On the one hand, the results suggest that the hypothesis was verified in terms of fatal risk per event; healthcare access, namely living close to harm reduction sites, seemed to reduce the likelihood of overdose death, which is consistent with previous findings. However, communities or regions without harm reduction sites had higher fatal risks than other places. On the other hand, the hypothesis was not justified (i.e., the risk of fatal overdose did not vary spatially) in the long term. Overdose survival was highly related to receiving OAT. Even though service access is limited in rural communities, people in these areas were more likely to have received OAT resulting in no significant difference in survival probability over space.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".