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Record W2295405970 · doi:10.1111/eip.12326

Comparative analysis of pathways to early intervention services and duration of untreated psychosis in two Canadian cities

2016· article· en· W2295405970 on OpenAlexafffundabout
Nina Flora, Kelly K. Anderson, Manuela Ferrari, Andrew Tuck, Suzanne Archie, Sean A. Kidd, Kwame McKenzie

Bibliographic record

VenueEarly Intervention in Psychiatry · 2016
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoMcMaster UniversityYork UniversityWestern UniversityCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsInterquartile rangeIntervention (counseling)Context (archaeology)PsychosisDuration (music)Care pathwayMedicinePsychiatryEarly psychosisHealth careGerontologyGeographyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

AIM: Understanding pathways to early intervention services for psychosis in the local context is crucial, as the structure and organization of services need to be considered. This study compared pathways to early intervention services in two Canadian cities. METHODS: Data on pathways to care and duration of untreated psychosis were collected from 171 people admitted to early intervention services in Toronto (n = 150) and Hamilton (n = 21). We compared the cities on several indicators of pathway to care and duration of untreated psychosis. RESULTS: Pathways to care were more complex in Toronto, where people saw a greater number of health care services (median = 6, interquartile range = 3-9) than those in Hamilton (median = 3, IQR = 1-4). General practitioner involvement was higher in Toronto (56.0% vs. 33.3%). We did not find differences in the median duration of untreated psychosis. CONCLUSIONS: Pathways to early intervention services could be streamlined, and general practitioners may be a target for strategies to improve pathways to care.

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 score0.632
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.020
GPT teacher head0.330
Teacher spread0.309 · 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

Citations8
Published2016
Admission routes3
Has abstractyes

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