Bibliographic record
Abstract
Supervised injection facilities (SIFs) are promising in the reduction of opioid overdoses — at least in the only two sites in the world where they have been studied — according to a January report issued by the Institute for Clinical and Economic Review (ICER). The report, a literature search, noted that there are no randomized controlled trials that would make it possible to compare SIFs, currently illegal in the United States, with syringe services programs, which are not only legal in most states but can receive federal funding. The ICER did find that SIFs are associated with a reduction of opioid overdoses in Vancouver, Canada, and Sydney, Australia. Whether they should open in the United States as a way to combat opioid overdose deaths is a subject of controversy. We asked Beau Kilmer, Ph.D., McCauley Chair in Drug Policy Innovation at RAND and director of the RAND Drug Policy Research Center, a top expert in the United States on the topic, to review the executive summary for us, which he kindly did. “I think they did a good job of highlighting (1) the issues with generalizability — the vast majority of the published research comes from two sites, and most of this research was conducted before illegally produced fentanyl hit the streets — and (2) the lack of strong research designs in many of these studies,” he told ADAW . Another review of the literature, published in Addiction in 2019, for which Kilmer was corresponding author, concluded:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".