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Record W2063273636 · doi:10.1007/s10464-011-9446-x

Correlates of Homeless Episodes Among Indigenous People

2011· article· en· W2063273636 on OpenAlexaboutno aff
Les B. Whitbeck, Devan M. Crawford, Kelley J. Sittner

Bibliographic record

VenueAmerican Journal of Community Psychology · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institute on Alcohol Abuse and AlcoholismHenry J. Kaiser Family Foundation
KeywordsHealth psychologyPsychiatryMental healthSubstance abusePsychologyLongitudinal studyPublic healthDepression (economics)IndigenousAlcohol abuseMultivariate analysisClinical psychologyMedicine

Abstract

fetched live from OpenAlex

This study reports the correlates of homeless episodes among 873 Indigenous adults who are part of an ongoing longitudinal study on four reservations in the Northern Midwest and four Canadian First Nation reserves. Descriptive analyses depict differences between those who have and have not experienced an episode of homelessness in their lifetimes. Multivariate analyses assess factors associated with a history of homeless episodes at the time of their first interview and differentiate correlates of "near homelessness" (i.e., doubling up) and "homeless episodes" (periods of actual homelessness). Results show that individuals with a history of homeless episodes had significantly more individual and family health, mental health, and substance abuse problems. Periods of homelessness also were associated with financial problems. Among the female caretakers who experienced episodes of homelessness over the course of the study, the majority had been homeless at least once prior to the start of the study and approximately one-fifth met criteria for lifetime alcohol dependence, drug abuse, or major depression. Family adversity during childhood was also common for women experiencing homelessness during the study.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.088
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.080
GPT teacher head0.428
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.

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

Citations22
Published2011
Admission routes1
Has abstractyes

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