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Record W2332369176 · doi:10.1097/nmd.0000000000000462

Substance Use Among Homeless Individuals With Schizophrenia and Bipolar Disorder

2016· article· en· W2332369176 on OpenAlexafffundabout
Angelo Giovanni Icro Maremmani, Silvia Bacciardi, Nicole D. Gehring, Luca Cambioli, Christian G. Schütz, Kerry L. Jang, Michael Krausz

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Advancing Health OutcomesUniversity of AlbertaUniversity of British Columbia
FundersHealth CanadaMental Health Commission
KeywordsPsychiatryBipolar disorderPsychosocialSchizophrenia (object-oriented programming)Mental illnessSubstance abuseSubstance usePopulationPsychologyClinical psychologyHallucinogenMedicineMental healthMood

Abstract

fetched live from OpenAlex

Mental illness and substance use are overrepresented within urban homeless populations. This paper compared substance use patterns between homeless individuals diagnosed with schizophrenia spectrum (SS) and bipolar disorders (BD) using the Mini-International Neuropsychiatric Interview. From a sample of 497 subjects drawn from Vancouver, Canada who participated in the At Home/Chez Soi study, 146 and 94 homeless individuals were identified as BD and SS, respectively. In the previous 12 months, a greater proportion of BD homeless reported greater use of cocaine (χ = 20.0, p = 0.000), amphetamines (χ = 13,8, p = 0.000), opiates (χ = 24.6, p = 0.000), hallucinogens (χ = 11.7, p = 0.000), cannabinoids (χ = 5.05, p = 0.034), and tranquilizers (χ = 7.95, p = 0.004) compared to SS. Cocaine and opiates were significantly associated with BD homeless (χ = 39.06, df = 2, p < 0.000). The present study illustrates the relationship between substance use and BD in a vulnerable urban population of homeless, affected by adverse psychosocial factors and severe psychiatric conditions.

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)0.000
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 score0.011
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.312
Teacher spread0.290 · 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

Citations54
Published2016
Admission routes3
Has abstractyes

Explore more

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