Addressing Housing Insecurity: Policy Analysis of Managed Care Organizations and Healthcare Providers
Bibliographic record
Abstract
An average of 4,000 Rhode Islanders experience homelessness and housing insecurity each year. A policy analysis of screening tools for Social Determinants of Health (SDOH) was conducted for Neighborhood Health Plan of Rhode Island (NHPRI), Inland Empire Health Plan, The Veteran Health Administration (VHA), The Medicaid Medical Directors Network (MMDN), Executive Office of Health and Human Services (EOHHS), RI Office of Housing and Community Development, U.S. Department of Housing and Urban Development, Keiser Permanent School of Medicine Health Systems Science, Canadian Observatory on Homelessness, and primary care offices in Indianapolis, Florida, and Indiana. The analysis revealed ineffective screening when addressing SDOH, specifically housing insecurity and homelessness. Ineffective SDOH screening within managed care organizations (MCOs) and by healthcare providers contributed to health instability. The average cost to Rhode Island taxpayers for emergency room visits for persons experiencing homelessness is $18,000 a year. A literature review of key evidence demonstrated that a standardized SDOH assessment screening tool is needed to determine the appropriate plan of care and treatment such as the Vulnerability Assessment Tool (VAT). A policy update for proper assessment and screening of SDOH is needed by local MCOs and healthcare providers to address the rising costs of healthcare and the increased needs of high-risk populations. Recommendations to adopt a similar version of the VAT as part of the health plan assessment is a possible solution towards increasing health equity.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".