A NEET distinction: youths not in employment, education or training follow different pathways to illness and care in psychosis
Bibliographic record
Abstract
PURPOSE: The early phases of psychosis, including the prodrome, often feature educational/occupational difficulties and various symptoms and signs, that can render or keep youths "Not in Employment, Education or Training" (NEET). Conversely, NEET status itself may increase risk for illness progression and impaired functioning, and impede access to appropriate services for psychosis. As these issues have not been investigated, we aimed to examine differences in the illness and care pathways and characteristics of youths with psychosis who are NEET and non-NEET. METHODS: Youths entering a catchment-based Canadian early intervention service for psychosis (N = 416) were assessed as being NEET or non-NEET and compared on symptomatology, premorbid adjustment, prodrome and duration of untreated psychosis (DUP). RESULTS: Thirty-nine percent of the sample was NEET. Compared to non-NEET youths, NEET youths had 34% higher negative symptoms scores, longer prodromes (median of 52 weeks vs. 24 weeks), and were more often continuously ill after their first psychiatric change until the onset of psychosis (62% vs. 45%). Both groups had similar premorbid adjustment scores until late adolescence when scores were significantly worse for NEET youths. Accounting for other predictors, NEET youths had 23% longer DUPs on average, despite having made more help-seeking attempts. CONCLUSIONS: Despite being more narrowly defined, NEET status was thrice as prevalent in our sample as in the Canadian population. The NEET group followed a distinct trajectory of persistent symptoms and functional decline before presenting with a psychotic disorder. The systemic delays that NEET youths encountered indicate a need for better-targeted early identification efforts.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".