A scoping review of surgical care for people experiencing homelessness: prevalence, access, and disparities
Bibliographic record
Abstract
BACKGROUND: Numerous studies have highlighted the inequitable access to medical and psychiatric care that people experiencing homelessness (PEH) face, yet the surgical needs of this population are not well understood. We sought to assess evidence describing surgical care for PEH and to perform a thematic analysis of the results. METHODS: Ovid MEDLINE, Embase, and Web of Science were searched using the terms "surgery" AND "homelessness." Grey literature was also searched. We used a stepwise scoping review methodology, followed by thematic analysis using an inductive approach. RESULTS: We included 104 articles in our review. Studies were included from 5 continents; 63% originated in the United States. All surgical specialties were represented with varying surgical conditions and procedures for each. Orthopedic surgery (21%) was the most frequently reported specialty. Themes identified included characteristics of PEH receiving surgical care, homeless-to-housed participants, interaction with the health care system, educational initiatives, barriers and challenges, and interventions and future strategies. CONCLUSION: We identified significant variation and gaps, representing opportunities for further research and interventions. Further addressing the barriers and challenges that PEH face when accessing surgical care can better address the needs of this population.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".