Parallel latent trajectories of mental health and employment earnings among 16- to 20-year-olds entering the US labor force: A 20-year longitudinal study
Bibliographic record
Abstract
Introduction Depression and anxiety-related mental health and employment earnings are complexly intertwined but have rarely been studied as parallel processes. Objectives Determine the number of latent parallel trajectories of mental health and employment earnings over two decades among a cohort of American youth entering the labor force, and estimate the association between baseline sociodemographic/health factors and latent trajectory class membership. Methods This study included 8,173 participants from the American National Longitudinal Survey of Youth 1997, who were 13–17 years old in 1997. The survey occurred annually until 2011 then biennially until 2017. Mental health was measured eight times using the Mental Health Inventory-5 between 2000–2017. Employment earnings were measured annually between 1998–2017, where participants were 33–37 years old. Latent parallel trajectories were estimated using latent growth modeling. The association between baseline predictors and trajectory membership was explored using multinomial logistic regression. Results Four latent trajectory classes were identified: good mental health, high earnings (3% of sample, average 2017 earnings ˜$196,000 USD); good mental health, medium earnings (23%, average 2017 earnings ˜$78,100); good mental health, low earnings (50%, average 2017 earnings ˜$39,500); and poor mental, low earnings (24%, average 2017 earnings ˜$32,000). Multinomial models revealed participants who were younger, female, Black, Hispanic, who had lower socioeconomic status, and had used marijuana at baseline had higher odds of belonging to the poor mental health, low earnings class. Conclusions Findings highlight the stagnated, parallel course of poor mental health and earnings, and the influence of gender, race, adolescent socioeconomic status, and health behaviors on these trajectories. Disclosure No significant relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".