Previous employment histories and quality of life in older ages: sequence analyses using SHARELIFE
Bibliographic record
Abstract
ABSTRACT This article summarizes previous employment histories and studies associations between types of histories and quality of life in older ages. Retrospective information from the Survey of Health, Ageing and Retirement in Europe (SHARE) was used and the occupational situation for each age between 30 and 65 of 4,808 men and 4,907 women aged 65 or older in Europe was considered. Similar histories were regrouped using sequence analyses, and multi-level modelling was applied to study associations with quality of life. To avoid reverse causality, individuals with poor health prior to or during their working life were excluded. Men's employment histories were dominated by long periods of paid employment that ended in retirement (‘regular’ histories). Women's histories were more diverse and also involved domestic work, either preceding regular careers (‘mixed’ histories) or dominating working life (‘home-maker’ histories). The highest quality of life was found among women with mixed histories and among men with regular histories and late retirement. In contrast, retirement between 55 and 60 (but not earlier) and regular histories ending in unemployment or domestic work (for men only) were related to lower quality of life, as well as home-maker histories in the case of women. Findings remain significant after controlling for social position, partnership and parental history, as well as income in older ages. Results point to the importance of continuous employment for health and wellbeing, not only during the working life, but also after labour market exit.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".