Academic retirement: A systematic review of the literature on retirement planning among university faculty
Bibliographic record
Abstract
Abstract The overarching goal of this study is to inform institutional policy, support systems, and future research focused on enhancing retirement transitions within academia. In this scoping review we: (1) explore key factors influencing the retirement decisions of higher education faculty, including common pathways and barriers faced by faculty; (2) synthesize prominent study designs and findings in this area; and (3) evaluate the current state of the literature. A scoping review was conducted to address the outlined objectives. Relevant studies were identified through comprehensive searches across multiple academic databases using terms related to retirement decisions among university faculty. Four independent reviewers screened the literature and extracted key data using a standardized process to ensure rigor and consistency in study selection and synthesis. A total of 74 studies met the inclusion criteria. Most utilized cross-sectional survey designs, with qualitative interviews also commonly employed. Frequently examined variables included chronological age, partner or spousal retirement timing, health status, institutional policies, and professional identity. Geographical focus and academic subfields varied across the literature, revealing gaps in cross-contextual understanding. A concentration on individual and organizational factors was noted, with limited longitudinal research capturing transitions over time. This review builds on prior work by highlighting the varied nature of retirement among academic professionals, who often work in environments characterized by high autonomy. Understanding these retirement processes is critical for designing policies and practices that support career satisfaction and institutional knowledge retention. The findings have implications for retirement education and the development of inclusive retirement policies.
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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 | 0.023 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".