Setting the scene: Cross-sectional country comparisons of associations between young adult care and education, employment, and health outcomes in Europe
Bibliographic record
Abstract
With increasing demand for informal carers, young adults are taking up care for dependent adults in their family and close networks. However, this may have important repercussions for their longer-term labour market and health outcomes. Early adulthood is the period in which most people invest in human capital and transition to employment. Being neither in employment nor in training (NEET) during this period may have long-term effects, increasing the risk of future unemployment and poor mental health. Furthermore, this can vary depending on the country availability of social support systems. Nonetheless, only a few studies have explored the association between taking care of a dependent adult during young adulthood and NEET and health outcomes in a cross-country perspective. This study explores data from the third wave of European Health Survey (EHIS, 2019) to better understand the situation of young adult carers in Europe. We explore the association between caregiving and NEET status, as well as self-perceived health and mental health of young adults (aged 18-29 years), using multilevel regression models to estimate country differences in these three associations. Like this, we can examine the extent to which formal care resources available in each country reduce the gap between carers and non-carers related to their health and NEET outcomes. Our results indicate that, overall, those who care for dependent adults are more likely to be in NEET status (only intensive caring), perceive bad health and report worse mental health. However, long-term state care resources do not affect the gap in any of our outcomes between carers and those who do not care. Hence, it may be cultural differences, or other forms of support, that play a role in the health risks of young adult carers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".