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Record W3157275106 · doi:10.1377/hlthaff.2020.01796

Temporary Financial Assistance Decreased Health Care Costs For Veterans Experiencing Housing Instability

2021· article· en· W3157275106 on OpenAlexaboutno aff
Richard E. Nelson, Ann Elizabeth Montgomery, Ying Suo, James Cook, Warren Pettey, Adi V. Gundlapalli, Tom Greene, William N. Evans, Lillian Gelberg, Stefan G. Kertesz, Jack Tsai, Thomas Byrne

Bibliographic record

VenueHealth Affairs · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersU.S. Department of Veterans Affairs
KeywordsQuarter (Canadian coin)Supportive housingVeterans AffairsEmergency departmentHealth careMedicineBusinessMedical emergencyGerontologyNursingEconomic growthEconomics

Abstract

fetched live from OpenAlex

Compared with housed people, those experiencing homelessness have longer and more expensive inpatient stays as well as more frequent emergency department visits. Efforts to provide stable housing situations for people experiencing homelessness could reduce health care costs. Through the Supportive Services for Veteran Families program, the Department of Veterans Affairs partners with community organizations to provide temporary financial assistance to veterans who are currently homeless or at imminent risk of becoming homeless. We examined the impact of temporary financial assistance on health care costs for veterans in the Supportive Services for Veteran Families program and found that, on average, people receiving the assistance incurred $352 lower health care costs per quarter than those who did not receive the assistance. These results can inform national policy debates regarding the proper solution to housing instability.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.057
GPT teacher head0.422
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2021
Admission routes1
Has abstractyes

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