CONTEMPORARY EARLY RETIREMENT FACTORS AND STRATEGIES TO ENCOURAGE AND ENABLE LONGER WORKING LIVES
Bibliographic record
Abstract
Abstract Accelerating population aging is raising concern in many countries now about the availability of workers for essential work roles and responsibilities. A scoping research literature review was done to identify factors currently associated with early retirement and contemporary strategies to encourage and support longer working lives. Among 53 relevant articles, seven early retirement factors were revealed: Ill health, good health, workplace issues, the work itself, ageism, social norms, and having achieved personal financial or pension requirement criteria. Six solutions, none of which had been proven effective, were identified: Occupational health programs, workplace enhancements, work adjustments, addressing ageism, changing social norms, and pension changes. The evidence base on early retirement prevention is not strong, with qualitative research studies needed to gain a more in-depth understanding of early retirement influences and also mixed-methods studies needed to test early retirement prevention solutions for their effects, and particularly in the healthcare sector as healthcare needs typically rise with advanced ageing. Until more evidence is available, every healthcare and other organization should perform an early retirement risk assessment and identify current versus needed policies and programs to encourage and to enable more middle-aged and older people to work longer in life.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".