Public and Community Perceptions of Safe Injections Sites
Bibliographic record
Abstract
The number of deaths by opioid overdose have quadrupled since 1999. In 2019 alone, there were about 50,000 deaths caused by overdoses and the numbers increase each year. While there are harm reduction techniques used to fight against this epidemic, they are clearly not sufficient as the number of deaths have been rising at staggering rates. A safe injection site is a facility that is staffed with medically trained individuals to operate a safe environment for those using injection drugs like opioids. In this systematic review of the literature is focused on obtaining the perceptions of the public specifically on safe injection sites. Two research databases, PubMed and Scopus, were utilized for this review. Peer-reviewed articles were screened based on specific eligibility criteria. Five peer-reviewed papers were selected for data extraction out of 261 screened articles. There were many similarities found amongst the papers but also differences. Two of the five studies found that more than half of participants were in favor of SIS. Political party affiliation was found to be the most likely correlating factor for support of SIS in four of the five papers. The differences amongst the results may be attributed to the vastly different countries including the US, France, and Canada. Data showing support for SIS in the US may be the start to potentially increasing their use against the opioid crisis in the US.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".