Evidence map›Paper›PMID 39120714›Full record

ArticleSocial psychiatry and psychiatric epidemiology2025

An intersectional perspective on the sociodemographic and clinical factors influencing the status of not in Education, Employment, or training (NEET) in patients with first-episode psychosis (FEP).

Jiaxuan Deng, Lisa Sarraf, Adèle Hotte-Meunier, Stéphanie El Asmar, Jai Shah, Ridha Joober, Ashok Malla, Srividya Iyer, Martin Lepage, Geneviève Sauvé

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Article in Social psychiatry and psychiatric epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jiaxuan DengDouglas Research Centre, Montreal, QC, Canada.
Lisa SarrafDepartment of Psychology, Carleton University, Ottawa, ON, Canada.
Adèle Hotte-MeunierDouglas Research Centre, Montreal, QC, Canada.
Stéphanie El AsmarDouglas Research Centre, Montreal, QC, Canada.
Jai ShahDouglas Research Centre, Montreal, QC, Canada.
Ridha JooberDouglas Research Centre, Montreal, QC, Canada.
Ashok MallaDouglas Research Centre, Montreal, QC, Canada.
Srividya IyerDouglas Research Centre, Montreal, QC, Canada.
Martin LepageDouglas Research Centre, Montreal, QC, Canada.
Geneviève SauvéDouglas Research Centre, Montreal, QC, Canada. sauve.genevieve@uqam.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeHigh rates of Not in Education, Employment or Training (NEET) are seen in people with first episode of psychosis (FEP). Sociodemographic and clinical factors were reported to be associated with NEET status in FEP patients. This study follows Intersectionality to examine the independent and additive effects, and most importantly the intersections of sociodemographic and clinical variables concerning NEET status in FEP patients. It was hypothesized that NEET status in FEP patients would be described by the intersection between at least two predictor variables.

methodsSecondary analyses with chi-square tests, multiple logistic regression and Chi-squared Automatic Interaction Detection (CHAID) analyses were performed on 440 participants with FEP.

resultsChi-square tests indicated that patient socioeconomic status and negative symptom severity were significantly and independently associated with their NEET status. Multiple logistic regression suggested additive effects of age (odds ratio = 1.61), patient socioeconomic status (odds ratio = 1.55) and negative symptom severity (odds ratio = 1.75) in predicting patients' NEET status. CHAID detected an intersection between patients' negative symptom severity and socioeconomic status in shaping their NEET status.

conclusionThis study explored how the NEET status of patients with FEP was explained not only by the separate effects of negative symptom severity and socioeconomic status but also by the unique intersections of their clinical and social identities. Findings indicated that functional outcomes of patients appear co-constructed by the intersections of multiple identities. Crucial clinical implications of complementing care for negative symptom severity with vocational resources to improve functional outcomes of patients are discussed.

Indexed as

EmploymentPsychotic DisordersAdolescentAdultEducational StatusFemaleHumansMaleMiddle AgedSeverity of Illness IndexYoung AdultDecision tree modelFunctional deteriorationIntersectionalitySchizophreniaSocial categoriesYouth

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.