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Record W2354000336 · doi:10.1016/j.eurpsy.2016.01.827

Multi-morbidity: Psychosis early childhood adversity and substance use within homeless people

2016· article· en· W2354000336 on OpenAlexaboutno aff
Michael Krausz

Bibliographic record

VenueEuropean Psychiatry · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)PsychiatryMental illnessDeclarationFoster careIntervention (counseling)Substance useMental healthSocial vulnerabilityPsychosisSubstance abusePsychologyMedicineNursingPolitical scienceComputer security

Abstract

fetched live from OpenAlex

Homelessness is the most visible indicator for social marginalization and vulnerability. It is a risk factor for subsequent health threats and especially individuals with a history of trauma, substance use and severe persistent mental illness are at high risk to loose their homes, jobs and social networks. The Canadian At Home/Chez Soi study aimed to better understand the entanglement of homelessness and mental illness and possible strategies to provide care to the most vulnerable. In 5 Canadian centers, over 2000 patients were included and randomized to different intervention arms based on a housing first approach. Early trauma and foster care were as rampant as poly substance use, which explains a significant increase in mortality too. Disclosure of interest The author has not supplied his declaration of competing interest.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.040
GPT teacher head0.324
Teacher spread0.285 · 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
Published2016
Admission routes1
Has abstractyes

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