EFFECTS OF SOCIAL VULNERABILITY AND EDUCATION ON FRAILTY AND COGNITION THROUGH WORK CHARACTERISTICS
Bibliographic record
Abstract
Background: Many jurisdictions are considering or have already increased pension eligibility age and more adults are remaining employed for longer. As such, understanding how work characteristics impact the health and well-being of older workers and the predisposing factors that may predict work quality is important. We sought to examine how education and social vulnerability are associated with job characteristics (i.e., job control, effort-reward imbalance) and, in turn, how job characteristics are associated with frailty and cognitive impairment. Methods: Participants were 3817 employed adults aged 50 years and older who participated in Waves 1 and 2 of the Survey of Health, Ageing, and Retirement in Europe. Using a structural equation modeling framework, we examined the direct and indirect effects – through job control and effort-reward imbalance - of education and social vulnerability on Wave 2 frailty and cognition. Results: Controlling for age and sex, social vulnerability had a direct effect, beta=0.23, p<.001, on frailty and direct, beta=0.15, p<.001, and indirect effects on cognitive impairment through job control, beta=0.03, p<.01, and effort-reward imbalance, beta=-0.03, p<.01. Education had a direct effect, beta=-0.19, p<.001, on cognitive impairment and indirect effects on frailty through both job control, beta=0.02, p< .01 and effort-reward imbalance, beta=-0.01, p<.05. Conclusion: We found that the characteristics of an older person’s job may influence their health. Those with low levels of education and higher social vulnerability are more likely to have poor quality jobs (i.e., low control, high effort and low reward), which may lead to worsening frailty and cognition.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".