An international review of skin conditions in incarcerated persons
Bibliographic record
Abstract
Introduction Incarceration negatively affects inmates' skin and overall quality of life. Overcrowded jail cells, poor hygiene, unsanitary conditions, and limited access to quality health care significantly increase the incidence of skin diseases among incarcerated persons (IPs). This review examines these diseases, their causes, and strategies to mitigate them. Methods A literature search using Google Scholar and PubMed covered studies from 2002 to 2023, identifying 18 peer-reviewed studies. These studies provided data on IP skin conditions, adherence to treatment, IP living conditions, health care access, and strategies to alleviate skin conditions. Results The studies were conducted in Australia, Cameroon, Canada, France, Guinea, India, Italy, Korea, Nepal, Nigeria, Pakistan, Taiwan, Turkey, and the United States. They consistently showed that inadequate health care, poor hygiene, overcrowding, and improper treatment protocols contribute to skin diseases in IP. Common conditions included scabies, fungal infections, acne, and psoriasis. Teledermatology has proven effective in reducing these conditions. Additional measures should focus on reducing overcrowding and improving hygiene practices. Conclusions IPs face numerous dermatological challenges that impact their health and well-being. Enhancing hygiene, improving on-site care with teledermatology, and implementing policy reforms can mitigate these issues from the standpoint of the prison health system director. Dermatologists from the community or academic centers must engage to prioritize the health care model or models that will work best in their region since they are responsible for diagnosing and treating IPs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".