Not in employment, education or training: Mental health, substance use, and disengagement in a multi-sectoral sample of service-seeking Canadian youth
Bibliographic record
Abstract
Youth who are not engaged in employment, education or training (NEET) face multiple health, economic and psychosocial challenges. Despite the popularity of the NEET metric internationally, there is a paucity of research describing Canadian NEET youth. The proportion of NEET youth aged 12 to 24 presenting for services across multiple service sectors in Canada was examined. Their sociodemographic characteristics and mental health concerns were compared with those of their non-NEET peers. Over a quarter of youth were NEET, and they presented for services across all sectors. NEET youth showed multiple psychosocial risk factors. They were also more likely to endorse substance use and crime/violence concerns than their non-NEET service-seeking counterparts. Gender-based differences were observed. Since many youth presenting for services across sectors are NEET, youth-serving agencies should be prepared to offer a wide range of services to address their diverse needs. Implications for systematic screening and integrated service provision are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".