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Record W4389688034 · doi:10.46747/cfp.6912859

Models of shared care for the management of psychotic disorder after first diagnosis in Ontario

2023· article· en· W4389688034 on OpenAlexafffundvenueabout
Rebecca Rodrigues, Jennifer Reid, Kelly K. Anderson

Bibliographic record

VenueCanadian Family Physician · 2023
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistryCanadian Institutes of Health ResearchAcademic Medical Organization of Southwestern OntarioLawson Health Research Institute
KeywordsMedicinePrimary careRetrospective cohort studyCohortPsychiatryPsychosisFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the provision of care for young people following first diagnosis of psychotic disorder. DESIGN: Retrospective cohort study using health administrative data. SETTING: Ontario. PARTICIPANTS: People aged 14 to 35 years with a first diagnosis of nonaffective psychotic disorder in Ontario between 2005 and 2015 (N=39,449). MAIN OUTCOME MEASURES: Models of care, defined by psychosis-related service contacts with primary care physicians and psychiatrists during the 2 years after first diagnosis of psychotic disorder. RESULTS: During the 2-year follow-up period, 29% of the cohort received only primary care, 30% received only psychiatric care, and 32% received both primary and psychiatric care (shared care). Among the shared care group, 72% received care predominantly from psychiatrists, 20% received care predominantly from primary care physicians, and 9% received approximately equal care from psychiatry and primary care. Variation in patient and physician characteristics was observed across the different models of care. CONCLUSION: One in 3 young people with psychotic disorder received shared care during the 2-year period after first diagnosis. The findings highlight opportunities for increasing collaboration between primary care physicians and psychiatrists to enhance the quality of care for those with early psychosis.

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 score0.654
Threshold uncertainty score0.689

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 head0.034
GPT teacher head0.267
Teacher spread0.233 · 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

Citations6
Published2023
Admission routes4
Has abstractyes

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