Describing the Medical Needs of Hawai‘i’s Houseless Population During COVID at Free Student Run Outpatient Clinics (Hawai‘i HOME Project)
Bibliographic record
Abstract
Hawai'i experiences some of the highest rates of houselessness per capita in the country. COVID-19 has exacerbated these disparities and made it difficult for these individuals to seek medical care. Hawai'i's Houseless Outreach in Medical Education (HOME) clinic is the largest student run free clinic in the state, which provides medical services to this patient population. This article reports the demographics, medical needs, and services provided to patients of Hawai'i's HOME clinic during the era of COVID-19. From September 2020 to 2021, the HOME clinic saw 1198 unique visits with 526 distinct patients. The most common chief complaints included wound care (42.4%), pain (26.9%), and skin complaints (15.7%). A large portion of the population suffered from comorbidities including elevated blood pressure (66%), a formal reported history of hypertension (30.6%), diabetes (11.6%), and psychiatric concerns including schizophrenia (5.2%) and generalized anxiety (5.1%). Additionally, a large portion of patients (57.2%) were substance users including 17.8% of patients endorsing use of alcohol, 48.5% tobacco and 12.5% marijuana. The most common services provided were dispensation of medication (58.7%), wound cleaning/dressing changes (30.7%), and alcohol or other drug cessation counseling (25.2%). This study emphasizes that the houseless are a diverse population with complex, evolving medical needs and a high prevalence of chronic diseases and comorbidities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".