Funding Health Care for People Experiencing Homelessness: An Examination of Federally Qualified Health Centers’ Funding Streams and Homeless Patients Served (2014–2019)
Bibliographic record
Abstract
It is estimated that three million people annually experience homelessness, with about a third of the homeless population being served by Federally Qualified Health Centers (FQHCs). Thus, FQHCs, dependent on government funding for financial viability, are vital to the infrastructure addressing the complex issues facing people experiencing homelessness. This study examines the relationship between various government funding streams and the number of homeless patients served by FQHCs. Data for this study come from three publicly available databases: the Uniform Data System (UDS), the IRS Core files, and the Area Resource File. Fixed-effects models employed examine changes across six years from 2014 to 2019. The results suggest that, on average, an additional homeless patient served increases the expenses of FQHCs more than other patients and that federal funding, specifically Health Care for the Homeless (HCH) funding, is a vital revenue source for FQHCs. We found that the number of homeless patients served is negatively associated with contemporaneous state and local funding but positively associated with substance use and anxiety disorders. Our findings have important implications for the effective management of FQHCs in the long term and for broader public policy supporting these vital elements of the social safety net.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".