Substance Use Service Utilization and Barriers to Access Among Homeless Veterans: A Scoping Review
Bibliographic record
Abstract
The high prevalence of military veteran substance use (SU) when compared to their nonveteran counterparts has been described as an urgent public health issue. The commonality of severe mental and physical health comorbidities in this population affects their ability to recover and relates to the onset and maintenance of homelessness. While veteran-targeted housing and SU interventions exist, they are being underutilized. This scoping review synthesizes published peer-reviewed articles from 1990 to 2021 at the intersections of housing, substance abuse, and service utilization by homeless veterans. Qualitative thematic analysis of 119 retained peer-reviewed articles revealed five key themes: (1) the association between SU and housing stability, (2) gendered comparisons with service needs and provision, (3) consideration for comorbidities, (4) social support and relationship-centered interventions, and (5) barriers to health care services. This review offers a series of concerns, outcomes, and recommendations that might be valuable for practitioners, health care providers, and community stakeholders when implementing or re-evaluating new or existing homeless veteran treatment programs.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".