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Record W4292337242 · doi:10.1002/hec.4579

The effects of supported housing for individuals with mental disorders

2022· article· en· W4292337242 on OpenAlexaff
Francisca Vargas Lopes, Pieter Bakx, Sam Harper, Bastian Ravesteijn, Tom Van Ourti

Bibliographic record

VenueHealth Economics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekErasmus Universiteit Rotterdam
KeywordsMental healthConsumption (sociology)Work (physics)Margin (machine learning)BusinessMental health careDemographic economicsPublic economicsEconomicsPsychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Societies face the challenge of providing appropriate arrangements for individuals who need living support due to their mental disorders. We estimate the effects of eligibility to the Dutch supported housing program (Beschermd Wonen), which offers a structured living environment in the community as an intermediate alternative to independent housing and inpatient care. For this, we use exogenous variation in eligibility based on conditionally random assignment of applications to assessors, and the universe of applications to supported housing in the Netherlands, linked to rich administrative data. Supported housing eligibility increases the probability of moving into supported housing and decreases the use of home care, resulting in higher total care expenditures. This increase is primarily due to the costs of supported housing, but potentially also higher consumption of curative mental health care. Supported housing eligibility reduces the total personal income and income from work. Findings do also suggest lower participation in the labor market by the individuals granted eligibility, but the labor participation of their parents increases in the long-run. Our study highlights the trade-offs of access to supported housing for those at the margin of eligibility, informing the design of long-term mental health care systems around the world.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.377
Teacher spread0.349 · 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 teacher head, not a consensus.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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