Mental health, education, and work in Canada, the Netherlands, and the United States: a comparative, life course investigation
Bibliographic record
Abstract
Canada, the Netherlands, and the United States differ in their social safety nets, education and labour market systems. This dissertation explores how these differences impact adolescent mental health and entry into higher education and work. The dissertation used population-level cohort data of individuals spanning adolescence to young adulthood. Findings show that across the three countries, adolescents exhibit consistent mental health patterns—with most experiencing low symptom levels over time but some experiencing increasing and/or decreasing symptom frequency—that may be related to physiological and social changes during the transition to adulthood. Higher levels of mental health problems at any point in adolescence are associated with more difficulties in education and work in young adulthood. <br/>Additionally, this dissertation suggests that how societies design their social welfare, education, and labour market systems affects the (un)equal distribution of adolescent mental health within a society and the potential for mental health problems to negatively impact education and work in young adulthood. <br/>The findings imply that adolescents with mental health problems will have greater success in education and the labour market as young adults if societies more equitably provide economic, educational and employment opportunities. The need for reforms in family, education, and labour market policy is particularly high in the USA. These findings may have implications for recovery from the COVID-19 pandemic given its mental health and labour market impacts. They stress the importance of examining and addressing gaps in the equitable provision of resources to young people with mental health problems who are transitioning to adulthood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".