Factors Contributing to Unexpected Retirement and Unemployment in Adults Over 50 Years Old in Ireland
Bibliographic record
Abstract
Older adults are increasingly important to maintaining stable workforces. As such, factors contributing to early workforce exit must be identified. This study aimed to identify predictors of unexpected retirement and unemployment at older age, with respect to psychological constructs, resulting adverse behaviors, and health-related factors reflecting functional status. Data were extracted from The Irish Longitudinal Study on Ageing (TILDA) to predict unexpected retirement and unemployment in older adults in Ireland. Increasing age, increasing number of impairments in activities of daily living, and frailty status of "pre-frail/frail" (relative to non-frail) increased the likelihood of unexpected retirement; while greater numbers of physical limitations and "pre-frail/frail" status significantly predicted unemployment at older age. Pre-frail/frail status or reduced physical capability for everyday tasks may adversely affect older individuals' ability to obtain and/or maintain employment. These findings advance the current understanding of factors associated with unexpected retirement and unemployment at older ages. Findings may aid in identifying strategies to extend working life and to aid at-risk older adults, and may inform components of care on which to focus to minimize loss of function and mobility, and maintain independence, with aging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".