Drivers of Being Unhoused and the Prevalence of Health Conditions among Unhoused Individuals in Asheville, NC
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, there was an increase in the number of unhoused individuals in Asheville, North Carolina resulting in more tent encampments.Understanding the physical, mental, and socially determined health characteristics associated with being unhoused can help guide stakeholders with policy development, healthcare program planning, and funding decisions to support unhoused individuals. Methods: In this study, we used an observational cross-section methodology. Using a convenience sample approach, we interviewed 101 participants who were receiving services from 2 emergency hotel shelters, a day center, and a resource center. Data were analyzed using descriptive statistics, and open-ended responses were collected and grouped to provide context. Results: Most participants were White (71%) and identified as male (76%). Over 60% reported having a high school education or advanced degree. Of the participants, 76% reported being unhoused for more than 6 months, and their last permanent housing was in Western North Carolina. Dental disease, chronic pain, and hypertension were common physical conditions. PTSD, depression, and anxiety were common mental health conditions. A lack of transportation was the most noted socially determined challenge. Marijuana, methamphetamine, and alcohol were the most often used substances, where methamphetamine was noted to be particularly problematic for the participants. Conclusion: Understanding the physical, mental, and social issues of the complex unhoused population can assist policymakers, healthcare providers, and other stakeholders in addressing challenges and testing improvement strategies.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".