Evaluating the Effects of Safe Injection Facility Legalization on Fatal and Non-Fatal Overdose and Infectious Disease
Bibliographic record
Abstract
In 2017, 70,000 lives were lost to fatal drug overdoses with approximately 46,000 of those involving the use of prescription and illicit opioids (CDC, 2018b). Unaddressed, the opioid epidemic is costing large amounts of money, lost productivity and valuable lives. Injection drug use has also become increasingly common in the United States, as it is an efficient means of consuming opioids. Unfortunately, injecting drugs is also an efficient method of transmitting bloodborne diseases. Estimates show that in the United States, 8% of all new HIV infections in 2010 and 22% of all adults and adolescents with HIV resulted from injection drug use (Lansky, 2014). Injecting drugs isn’t as uncommon as some might thing. Though it can be difficult to estimate the number of people who inject drugs, it has been reported somewhere between 4.5 and 8.6 million people inject drugs (Lansky, 2014). As there has been an increase in this behavior, the prevalence of infectious diseases spread through contact with blood have increased (Meiman, 2015). The continued rise in rates of injection drug use (IDU), and subsequent infectious disease indicate the need for a response from the United States government. One evidence-based strategy for reducing the health consequences of injection drug use is the implementation of safe injection facilities, which have been legalized and/or decriminalized in the Netherlands, Norway, Canada and 9 other nations. In this capstone, I will examine the potential impacts of legalizing safe injection facilities in the United States on non- fatal overdose, fatal overdose, HCV and HIV. I will also discuss the current United States federal law that would need to change or not be enforced in order to open and operate safe injection facilities without risk of prosecution.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".