Caught in the “NEET Trap”: The Intersection Between Vocational Inactivity and Disengagement From an Early Intervention Service for Psychosis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".