Street Medicine: A Scoping Review of Program Elements
Bibliographic record
Abstract
Globally, homelessness has grown over the past decade due to a diverse range of social and economic factors, including lack of affordable housing and retrenchment of social programs. People experiencing homelessness (PEH) experience markedly higher disease burdens than housed populations, as well as reduced access to healthcare. Street Medicine is a promising form of healthcare delivery that seeks to address the healthcare needs of this population despite known structural barriers to access. However, there is limited empirical literature describing Street Medicine practice and program elements. To address this gap, scoping review methods were used to review two major academic databases, web-based literature, and sources provided by content experts. Studies had to specifically reference “Street Medicine” as healthcare delivery and at least one of the review’s primary focuses: definitions, philosophies, or program elements. We conducted qualitative analysis to identify relevant themes. Of 1,016 unique sources identified, 349 met inclusion criteria. Six practice categories were identified: direct care, wraparound health services, harm reduction services, psychosocial case management, provision of life necessities, and education/research/advocacy. Despite a shared definition, there are a wide variety of Street Medicine program elements in use. This review sets parameters around which future research on effectiveness of these elements could be based.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".