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Record W2912083297 · doi:10.1176/appi.ps.201800319

Caught in the “NEET Trap”: The Intersection Between Vocational Inactivity and Disengagement From an Early Intervention Service for Psychosis

2019· article· en· W2912083297 on OpenAlex
Anika Maraj, Sally Mustafa, Ridha Joober, Ashok Malla, Jai Shah, Srividya N. Iyer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePsychiatric Services · 2019
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsDouglas Mental Health University Institute
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchMcGill UniversityBristol-Myers Squibb CanadaH. Lundbeck A/SCanada Research ChairsPfizer
KeywordsDisengagement theoryHazard ratioConfidence intervalMedicineIntervention (counseling)Proportional hazards modelVocational educationPsychiatryPsychosisInternal medicinePsychologyDemographyGerontology

Abstract

fetched live from OpenAlex

Objective: Given the benefits of early intervention for psychosis and the social disengagement of youths not in education, employment, or training (NEET), this study sought to examine how being vocationally inactive (NEET) affects engagement in early intervention services. Both baseline vocational status and vocational trajectory in the first year of treatment were analyzed. Methods: Data from 394 patients of a Canadian early intervention service were analyzed using time-to-event and Cox proportional hazards regression analyses. Two-year disengagement rates were compared between patients who were vocationally inactive and active at baseline and between those who remained vocationally inactive until month 12 and those who were vocationally inactive only at baseline. Pertinent sociodemographic (age, sex, visible minority status, social and material deprivation indices, and family involvement), and clinical (duration of untreated psychosis, substance use disorder, medication nonadherence, and baseline positive and negative symptoms) factors were considered. Results: There was no statistically significant difference between the disengagement rates of those who were vocationally inactive (N=154) and those who were vocationally active (N=240) at baseline. Those who remained vocationally inactive at month 12 (N=77) were likelier to disengage in the second year than those who were vocationally inactive only at baseline (N=48) (χ2=5.44, df=1, p<0.05). This comparison remained significant in the regression analysis (hazard ratio [HR]=8.52, 95% confidence interval [95% CI]=1.54–47.1). The association of disengagement from services with lack of family contact with the treatment team (HR=3.91, 95% CI=0.98–15.6) and with greater material deprivation (HR=1.03, 95% CI=1.00–1.07) trended toward significance. Conclusions: The functional recovery of youths who are vocationally inactive when they enter services can affect their long-term service engagement and merits targeting by evidence-based interventions.

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.

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 score0.022
Threshold uncertainty score0.998

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 head0.021
GPT teacher head0.327
Teacher spread0.306 · 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