Mental health inequalities during the second COVID-19 wave among Millennials who grew up in England: Evidence from the Next Steps cohort study
Bibliographic record
Abstract
BACKGROUND: There is relatively little evidence on socioeconomic inequalities in mental health among young adults after the end of the first COVID-19 wave in the UK, despite this group having faced the worse mental health and economic shocks across age groups at the start of the pandemic. METHODS: We examined differences in mental health across two points - September 2020 and February 2021 - in a cohort of 4167 Millennials aged 30-31 using life dissatisfaction, psychological distress (GHQ-12), anxiety (GAD-2), and depressive symptoms (PHQ-2). We report adjusted prevalence ratios (aPR) from random-intercept models, testing differences by educational attainment and time-varying conditions (relationship status, living arrangements with adults and children, work status, and financial changes compared with before the outbreak), adjusting for baseline covariates at ages 13-14 and health covariates at ages 25-26. RESULTS: Only dissatisfaction with life changed between time points (PR = 1.26, 95%CI 1.02-1.55). Educational attainment was not significantly associated with mental health. Being single (aPRs from 1.36 to 1.89) and being financially worse off since the start of the pandemic (aPRs from 1.58 to 1.76) were each associated with worse mental health. These associations did not further vary by educational attainment. CONCLUSION: Among Millennials who grew up in England, educational attainment was not associated with mental health whereas negative social and financial conditions were associated with worse mental health during the second COVID-19 wave. Mental health inequalities in this generation are likely to have continued increasing after the end of the first COVID-19 wave.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".