Harm Reduction 'On the Move': What Is the Role of Environmental Influences?
Bibliographic record
Abstract
Many harm-reduction services are provided through mobile programs (e.g., vans traveling to various locations), and such services are particularly important for reaching people who use substances who are socially and economically marginalized. Mobile harm reduction is not, however, a given but is shaped by the environment within which it occurs. Based on peer-reviewed literature, grey literature, and media reports primarily from Canada and the United States, we point to environmental conditions (e.g., limited funds for harm reduction, stigmatization of substance use) that appear to force mobile harm reduction, and influence (directly or indirectly) the route and speed of mobility, when and how it stops, as well as how it is experienced by harm-reduction workers and people who use substances. It is argued that there is a need to examine how environmental conditions in various places influence mobile harm reduction, including potential differences in impacts on harm-reduction workers' experiences, and service provision.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".