Differential Employment Quality and Educational Inequities in Mental Health: A Causal Mediation Analysis
Bibliographic record
Abstract
BACKGROUND: In the United States, inequities in mental distress between those more and less educated have widened over recent years. Employment quality, a multidimensional construct reflecting the relational and contractual features of employer-employee relationships, may mediate this inequity throughout adulthood, yet no study has examined the extent of this mediation in the United States, or how it varies across racialized and gendered populations. METHODS: Using the information on working-age adults from the 2001 to 2019 Panel Study of Income Dynamics, we construct a composite measure of employment quality via principal component analysis. Using this measure and the parametric mediational g-formula, we then estimate randomized interventional analogs for natural direct and indirect effects of low baseline educational attainment (≤high school: no/yes) on the end-of-follow-up prevalence of moderate mental distress (Kessler-6 Score ≥5: no/yes) overall and within subgroups by race and gender. RESULTS: We estimate that low educational attainment would result in a 5.3% greater absolute prevalence of moderate mental distress at the end of follow-up (randomized total effect: 5.3%, 95% CI = 2.2%, 8.4%), with approximately 32% of this effect mediated by differences in employment quality (indirect effect: 1.7%, 95% CI = 1.0%, 2.5%). The results of subgroup analyses across race and gender are consistent with the hypothesis of mediation by employment quality, though not when selecting on full employment (indirect effect: 0.6%, 95% CI = -1.0%, 2.6%). CONCLUSIONS: We estimate that approximately one-third of US educational inequities in mental distress may be mediated by differences in employment quality.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".