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Care Pathways and Initial Engagement in Early Psychosis Intervention Services Among Youths and Young Adults

2023· article· en· W4386703711 on OpenAlexafffund
Alexia Polillo, George Foussias, Wei Wang, Aristotle N. Voineskos, Jacqueline Veras, Nicole Davis-Faroque, Albert H.C. Wong, Nicole Kozloff

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthUniversity of TorontoCanadian Institutes of Health ResearchFondation Brain CanadaCentre for Addiction and Mental Health FoundationWellcome Trust
KeywordsReferralAttendanceMedicineEthnic groupFamily medicinePopulationIntervention (counseling)Medical recordCohortPsychiatryDemographyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Broad efforts to improve access to early psychosis intervention (EPI) services may not address health disparities in pathways to care and initial engagement in treatment. Objective: To understand factors associated with referral from acute hospital-based settings and initial engagement in EPI services. Design, Setting, and Participants: This retrospective cohort study used electronic medical record data from all patients aged 16 to 29 years who were referred to a large EPI program between January 2018 and December 2019. Statistical analysis was performed from March 2022 to February 2023. Exposures: Patients self-reported demographic information in a structured questionnaire. The main outcome for the first research question (referral source) was an exposure for the second research question (initial attendance). Main Outcomes and Measures: Rate of EPI referral from acute pathways compared with other referral sources, and rate of attendance at the consultation appointment. Results: The final study population included 999 unique patient referrals. At referral, patients were a mean (SD) age of 22.5 (3.5) years; 654 (65.5%) identified as male, 323 (32.3%) female, and 22 (2.2%) transgender, 2-spirit, nonbinary, do not know, or prefer not to answer; 199 (19.9%) identified as Asian, 176 (17.6%) Black, 384 (38.4%) White, and 167 (16.7%) other racial or ethnic groups, do not know, or prefer not to answer. Participants more likely to be referred to EPI services from inpatient units included those who were older (relative risk ratio [RRR], 1.10; 95% CI, 1.05-1.15) and those who identified as Black (RRR, 2.11; 95% CI, 1.38-3.22) or belonging to other minoritized racial or ethnic groups (RRR, 1.79; 95% CI, 1.14-2.79) compared with White participants. Older patients (RRR, 1.16; 95% CI, 1.11-1.22) and those who identified as Black (RRR, 1.67; 95% CI, 1.04-2.70) or belonging to other minoritized racial or ethnic groups (RRR, 2.11; 95% CI, 1.33-3.36) were more likely to be referred from the emergency department (ED) compared with White participants, whereas participants who identified as female (RRR, 0.51 95% CI, 0.34-.74) had a lower risk of ED referral compared with male participants. Being older (odds ratio [OR], 0.95; 95% CI, 0.90-1.00) and referred from the ED (OR, 0.40; 95% CI, 0.27-0.58) were associated with decreased odds of attendance at the consultation appointment. Conclusions and relevance: In this cohort study of patients referred to EPI services, disparities existed in referral pathways and initial engagement in services. Improving entry into EPI services may help facilitate a key step on the path to recovery among youths and young adults with 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)0.001
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 score0.094
Threshold uncertainty score0.724

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 head0.031
GPT teacher head0.324
Teacher spread0.292 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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