Vending machines for harm reduction and community health: a systematic review
Bibliographic record
Abstract
Context: In response to the growing overdose epidemic, communities in US and Canada have started using vending machines (VMs) as a low-barrier method to provide harm reduction (HR) items to the community. These VMs are becoming popular and typically dispense naloxone, drug testing strips, and other items for co-occurring conditions. Objective: To summarize and evaluate existing literature on VMs’ ability to deliver HR and other health items. Study Design, Analysis: Systematic literature review. For each eligible article, data was extracted and summarized on study design, goals, VM description, setting, location, target population, sample, results, outcome measures, and limitations. Dataset: Embase, Cochrane, PubMED, and MEDLINE (searched from inception to November 29, 2023); references of eligible articles and prior reviews. HR organizations were also contacted to share relevant articles on HR-based VMs. Population Studied: Individuals with substance use disorder or co-occurring conditions. Intervention: VMs or automated dispensing machines dispensing HR and/or health items. Outcome Measures: Feasibility, acceptability, reach, impact. Results: Of the 43 eligible articles, few were based in North America (n=10). Most VMs served those who injected drugs (n=27) and dispensed syringes (n=21). Other items included HIV self-tests (n=6), condoms (n=6), naloxone (n=2), and nicotine gum (n=1). Feasibility was most commonly evaluated (n=34), with high demand for VM-dispensed items, especially after business hours. Compared to in-person outreach, some VMs were able to provide more syringes and HIV self-tests. VMs were acceptable (n=20), regardless of item dispensed, and reached high-risk populations (n=14). Outcome measures for impact evaluation (n=16) varied on the dispensed item. Articles evaluating syringe-dispensing VMs’ impact (n=7) noted decreased syringe sharing (n=4) and drug use (n=2), while those evaluating naloxone-dispensing VMs (n=2) reported fewer fatal overdoses. Conclusions: VMs providing HR-related items are a promising community-based intervention to reach underserved populations and improve health outcomes. While HR-based VMs have been commonly studied and implemented, VMs could expand into other realms of community health, such as self-sampling kits for detection of cancer or sexually transmitted diseases. Future studies should utilize implementation science frameworks to develop and evaluate the VMs, with an emphasis on health outcomes.
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 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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".