Understanding Dermatologic Concerns Among Persons Experiencing Homelessness: A Scoping Review and Discussion for Improved Delivery of Care
Bibliographic record
Abstract
There is a paucity of information surrounding dermatologic care for persons experiencing homelessness (PEH). This scoping review aims to map existing literature and provide a summary of the most common cutaneous manifestations among PEH, risk factors for dermatologic disease, describe any reported interventions, as well as identify research gaps for future studies. Search strategies developed for MEDLINE and hand searching yielded 486 articles. Out of the 486 articles screened, 93 articles met the inclusion criteria. The majority were cohort studies, cross-sectional studies, and case-control studies concentrated in North America and Europe. Excluding the pediatric population, the prevalence of dermatologic conditions ranged from 16.6% to 53.5%. Common skin conditions described in PEH were: acne, psoriasis, seborrheic dermatitis, atopic dermatitis, and lichen simplex chronicus. There were no studies comparing the extent or severity of these cutaneous diseases in PEH and the general population. PEH have a higher prevalence of skin infections and non-melanoma skin cancers. This scoping review has direct implications on public health interventions for PEH and highlights the need for evidence-based interventions to provide optimum and safe dermatologic healthcare for PEH. We propose several recommendations for improved care delivery, including addressing upstream factors and comorbidities impacting skin health, providing trauma informed care, reducing barriers to care, preventing and managing skin conditions, as well as including PEH in the planning and implementation of any proposed intervention.
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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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".