Scoping out the literature on mobile needle and syringe programs—review of service delivery and client characteristics, operation, utilization, referrals, and impact
Bibliographic record
Abstract
BACKGROUND: Needle and syringe program (NSP) service delivery models encompass fixed sites, mobile services, vending machines, pharmacies, peer NSPs, street outreach, and inter-organizational agreements to add NSP services to other programs. For programs seeking to implement or improve mobile services, access to a synthesis of the evidence related to mobile services is beneficial, but lacking. METHODS: We used a scoping study method to search MEDLINE, PSYCHInfo, Embase, Scopus, and Sociological for relevant literature. We identified 39 relevant manuscripts published between 1975 and November 2017 after removing duplicates and non-relevant manuscripts from the 1313 identified by the search. RESULTS: Charting of the data showed that these publications reported findings related to the service delivery model characteristics, client characteristics, service utilization, specialized interventions offered on mobile NSPs, linking clients to other services, and impact on injection risk behaviors. Mobile NSPs are implemented in high-, medium-, and low-income countries; provide equipment distribution and many other harm reduction services; face limitations to service complement, confidentiality, and duration of interactions imposed by physical space; adapt to changes in locations and types of drug use; attract people who engage in high-risk/intensity injection behavior and who are often not reached by other service models; and may lead to reduced injection-related risks. DISCUSSION: It is not clear from the literature reviewed, what are, or if there are, a "core and essential" complement of services that mobile NSPs should offer. Decisions about service complement for mobile NSPs need to be made in relation to the context and also other available services. Reports of client visits to mobile NSP provide a picture of the volume and frequency of utilization but are difficult to compare given varied measures and reference periods. CONCLUSION: Mobile NSPs have an important role to play in improving HIV and HCV prevention efforts across the world. However, more work is needed to create clearer assessment metrics and to improve access to NSP services across the world.
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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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".