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Record W6904704032 · doi:10.14288/1.0368914

Eviction and loss of income assistance among street-involved youth in Canada

2018· article· en· W6904704032 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsEvictionSocial assistancePublic assistanceCohortIncome SupportLow incomeHousehold incomePermanent income hypothesisNational Longitudinal Surveys

Abstract

fetched live from OpenAlex

Loss of housing and income assistance among vulnerable youth has not been well described in the literature, yet it is a crucial issue for public health. This study examines the prevalence and correlates of loss of income assistance as well as eviction among street-involved youth. We collected data from a prospective cohort of street-involved youth aged 14–26. Among 770 participants, 64.3% reported having housing and 77.1% reported receiving income assistance at some point during the study period. Further, 28.6% and 20.0% of youth reported having been evicted and losing income assistance, respectively. In multivariable generalized estimating equations (GEE) analysis, heavy alcohol use, unprotected sex, being a victim of violence, and homelessness were all independently associated with eviction. Separately, homelessness, recent incarceration, and drug dealing were independently associated with loss of income assistance. Eviction and loss of income assistance are common experiences among street-involved youth with multiple vulnerabilities. Our findings highlight the importance of improving continued engagement with critical social services.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.025
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.354
Teacher spread0.313 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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