Leveraging Public Healthcare Dollars to Fund Critical Time Intervention: A Proposal for a Scalable Solution to Crisis Homelessness in the United States
Bibliographic record
Abstract
Recent experience demonstrates that substantial progress in reducing homelessness is possible if resources are directed towards evidence-based, housing-focused solutions. However, the homeless assistance systems in most countries are not adequately resourced to assist persons experiencing “crisis homelessness,” who account for the majority of the homeless population. Thus, we present a policy proposal for leveraging an expansion of Critical Time Intervention (CTI), an evidence-based behavioral health intervention, as a scalable solution for crisis homelessness. We draw on the specific policy context of the United States in proposing the use of funds from Medicaid, the public health insurance program for low-income individuals, to finance such an expansion, but argue our broader policy proposal has salience internationally as well. In presenting our proposal, we discuss why it represents a sound and feasible policy idea, focusing on the alignment between CTI and a promising new programmatic approach known as rapid rehousing. We describe the potential benefits of enacting this proposal and conclude with discussing the United States-specific and more general challenges that would need to be addressed to implement it, including the need for additional resources to cover the costs of the temporary financial assistance component of rapid re-housing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".