Housing and overdose: an opportunity for the scale-up of overdose prevention interventions?
Bibliographic record
Abstract
BACKGROUND: North America is currently experiencing an overdose epidemic due to a significant increase of fentanyl-adulterated opioids and related analogs. Multiple jurisdictions have declared a public health emergency given the increasing number of overdose deaths. In the province of British Columbia (BC) in Canada, people who use drugs and who are unstably housed are disproportionately affected by a rising overdose crisis, with close to 90% of overdose deaths occurring indoors. Despite this alarming number, overdose prevention and response interventions have yet to be widely implemented in a range of housing settings. OVERDOSE PREVENTION INTERVENTIONS: There are few examples of overdose prevention interventions in housing environments. In BC, for example, there are peer-led naloxone training and distribution programs targeted at some housing environments. There are also "supervised" spaces such as overdose prevention sites (similar to supervised consumption sites (SCS)) located in some housing environments; however, their coverage remains limited and the impacts of these programs are unclear due to the lack of evaluation work undertaken to date. A small number of SCS exist globally in housing environments (e.g., Germany), but like overdose prevention sites in BC, little is known about the design or effectiveness, as they remain under-evaluated. CONCLUSIONS: Implementing SCS and other overdose prevention interventions across a range of housing sites provides multiple opportunities to address overdose risk and drug-related harms for marginalized people who use drugs. Given the current overdose crisis rising across North America, and the growing evidence of the relationship between housing and overdose, the continued implementation and evaluation of novel overdose prevention interventions in housing environments should be a public health priority. A failure to do so will simply perpetuate what has proven to be a devastating epidemic of preventable death.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".