The Mental Health of Young Canadians Who Are Not Working or in School
Bibliographic record
Abstract
OBJECTIVE: Recent studies suggest that youth who have a mental health problem are more likely to be NEET-not in education, employment, or training-but findings remain mixed, and evidence from Canada is limited. We examined this association across a range of mental and substance disorders in a representative sample of Canadian youth. METHOD: Data were from the 2012 Canadian Community Health Survey-Mental Health ( n = 5622; ages 15-29). The survey identified past-year mental (depression, bipolar, generalized anxiety) and substance (alcohol, cannabis, other drugs) disorders from a structured interview and included questions on suicidal ideation. We classified as NEET respondents who were not in school or employed in the past week. Logistic regression models tested the associations between mental and substance disorders and NEET status, adjusted for sociodemographic, health, and geographic variables. RESULTS: About 10% of youth were NEET. Being NEET was associated with past-year depression (odds ratio [OR] = 1.67; 95% confidence interval [CI], 1.06 to 2.63); bipolar (OR = 2.31; 95% CI, 0.98 to 5.45), generalized anxiety (OR = 2.65; 95% CI, 1.37 to 5.12), and drug use (OR = 3.22; 95% CI, 1.33 to 7.76) disorders; and suicidal ideation (OR = 1.75; 95% CI, 0.99 to 3.09) but was not associated with alcohol (OR = 1.03; 95% CI, 0.63 to 1.69) or cannabis (OR = 0.97; 95% CI, 0.47 to 2.00) disorders. CONCLUSIONS: Poor mental health was associated with being NEET in Canadian youth. Efforts targeting NEET should include provisions for mental health. Moreover, youth mental health initiatives should consider educational and employment outcomes. Further longitudinal and intervention studies are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".