Bibliographic record
Abstract
Though the vulnerabilities and disadvantages of youths not in employment, education or training (NEET) are widely acknowledged, little mental health research has focused on the conditions that precede youths disengaging from school or work; how and to what extent such disengagement intersects with mental illness; and how early functional disengagement impacts overall outcomes.This symposium addresses these knowledge gaps.Sloan describes the post-secondary outcomes of young adults with emotional and behavioral disorders.Using data from an American longitudinal epidemiological study, she illustrates the interplay of sociodemography, diagnoses and outcomes in youths who had psychiatric disorders by age 16.Healy presents an Irish community-based investigation of the effects of childhood psychotic experiences on global functioning.Compared to children who reported no psychotic experiences, those who did had poorer functioning that persisted into early adulthood.Notably, their underlying global vulnerability extended beyond diagnosable disorders.Henderson shows that NEET youths presenting at Canadian clinical and non-clinical services were likelier to have concurrent mental health and substance use concerns, legal troubles and crime/violence problems; be in unstable housing situations; and need welfare aid.Iyer paints a picture of NEET youths at a Canadian early psychosis program having longer prodromes characterised by unrelenting illness and plummeting premorbid adjustment.This, along with longer durations of untreated psychosis, suggests that NEET youths follow a distinct trajectory of clinical and functional decline.Finally, Cannon (discussant) will situate these findings within what is currently known about youth mental illness and functioning, and highlight their services, research and policy implications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.524 | 0.331 |
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".