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Record W2136701713 · doi:10.1192/bjp.bp.111.097865

Ethnic variations in pathways into early intervention services for psychosis

2012· article· en· W2136701713 on OpenAlexaff
Sharif Ghali, Helen L. Fisher, John Joyce, Barnaby Major, Lorna Hobbs, Sujata Soni, Brock Chisholm, Nikola Rahaman, Peggy Papada, Jo Lawrence, Sally Bloy, Karl Marlowe, Katherine J. Aitchison, Paddy Power, Sonia Johnson

Bibliographic record

VenueThe British Journal of Psychiatry · 2012
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsdupEthnic groupIntervention (counseling)Context (archaeology)PsychiatryCriminal justicePsychosisMedicineCohortSocial environmentPsychologyCriminologyGeographyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Ethnic variations have previously been identified in the duration of untreated psychosis (DUP) and pathways into psychiatric services. These have not been examined in the context of early intervention services, which may alter these trajectories. AIMS: To explore ethnic differences in the nature and duration of pathways into early intervention services. METHOD: In a naturalistic cohort study, data were collected for 1024 individuals with psychotic disorders accepted for case management by eight London early intervention services. RESULTS: Duration of untreated psychosis was prolonged in the White British group compared with most other ethnic groups. White British individuals were more likely to make contact with their general practitioner and less likely to be seen within emergency medical services. All Black patient groups were more likely than their White British counterparts to experience involvement of criminal justice agencies. CONCLUSIONS: Variations continue to exist in how and when individuals from different ethnic groups access early intervention services. These may account for disparities in DUP.

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 score0.354
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.330
Teacher spread0.303 · 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

Citations59
Published2012
Admission routes1
Has abstractyes

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