Identifying contemporary early retirement factors and strategies to encourage and enable longer working lives: A scoping review
Bibliographic record
Abstract
AIM: Accelerating population ageing is raising concern in many countries now in relation to 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. METHODS: Using the PRISMA-ScR Checklist, we searched the Directory of Open Access Journals and EBSCO Discovery Service for published 2013-2018 research articles using the keyword/MeSH term "early retirement"; 54 English-language articles in peer-review journals were reviewed. RESULTS: 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 suggested solutions, none proven effective, were identified: Occupational health programmes, workplace enhancements, work adjustments, addressing ageism, changing social norms and pension changes. CONCLUSIONS: The evidence base on early retirement prevention is not strong, with qualitative investigations needed for in-depth understandings of early retirement influences and mixed-methods studies needed to test early retirement prevention solutions for their effects. IMPLICATIONS FOR PRACTICE: Until more evidence is available, every organisation should perform an early retirement risk assessment and identify current versus needed policies and programmes to encourage and enable more middle-aged and older people to work longer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".