A Qualitative Exploration of People Who Have Left Housing First and Returned to Homelessness
Bibliographic record
Abstract
This article offers a thick, rich, multidimensional, and situational look into a Housing First (HF) program in Charlotte, North Carolina, called HousingWorks. This study focuses on individuals who have experienced chronic homelessness and who exited permanent supportive housing (PSH) programs that employ a HF model and return to homelessness. In January 2022, there were 582,500 people experiencing homelessness in the United States (The U.S Department of Housing and Urban Development [HUD], 2023). One-third of these individuals had patterns of chronic homelessness, which has doubled since 2016 (HUD, 2023). Additionally, one-third of unsheltered individuals are at a high risk of being unsheltered again. To address issues of chronic homelessness, emphasis has been placed on increasing supportive housing, which combines permanent affordable housing with supportive services (permanent supportive housing), commonly called a housing first, or the rapid rehousing approach. Participants exited HousingWorks for multiple individual and programmatic reasons. All exits in this study were tied to relationships individuals had with friends, family (of choice and biological), romantic partners, case managers, and neighbors. Consumers, providers, and former residents describe their experiences and offer insights for improving housing retention in PSH programs. Implications for implementation and housing first fidelity criteria are also discussed.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".