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Record W2901041964 · doi:10.1093/geroni/igy031.3616

PROFILES OF OLDER HOMELESS ADULTS WITH CHRONIC MENTAL HEALTH PROBLEMS IN LOS ANGELES COUNTY

2018· article· en· W2901041964 on OpenAlexaboutno aff
Christopher Clark, Jennifer Ailshire

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthGerontologyEthnic groupQuarter (Canadian coin)PopulationMedicineMental illnessPsychologyPsychiatryEnvironmental healthGeography

Abstract

fetched live from OpenAlex

Homeless adults with ongoing mental health problems are a particularly vulnerable segment of the older adult population, but the characteristics and conditions of this population remain largely unknown to aging researchers. We use data on 2,169 unsheltered adults over age 50 from the 2017 Los Angeles County Homeless Survey to examine the characteristics of mentally ill older homeless adults, their pathways to homelessness, and their service needs. Chronic mental health problems were prevalent in 48% of older adults, with higher prevalence among women (54%) compared to men (47%) and those ages 50–64 (49%) compared to those 65+ (42%). About one-quarter also had drug or alcohol problems or physical illness and 41% also had a disability. Results from multivariate models adjusted for age, gender, race/ethnicity and education show those with mental health problems are more likely to have had their first experience of homelessness prior to age 50 and are more likely to be living in a tent. The primary reasons identified for loss of housing related to medical conditions, drug use, and mental health. Respondents were asked to rate the importance of a variety of services. Those with mental health problems were more likely to identify as being important or very important services related to transportation, legal, physical and mental health, drug treatment, disability, and family counseling. It’s critically important to address the needs of this largely invisible, but incredibly vulnerable, older adult population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.031
GPT teacher head0.379
Teacher spread0.348 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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