A scoping review and synthesis of the literature on retirement among academic health professionals
Bibliographic record
Abstract
Abstract Background Interest in planning for retirement among academic health professionals has grown in recent years as more people approach traditional retirement age. This scoping review examines the current literature on retirement planning and retirement perceptions among academic health professionals, identifies key themes, and evaluates the quality of peer-reviewed studies. Research design and methods A comprehensive search across Medline, Embase, APA PsycInfo, AgeLine, ERIC, Education Source, Business Source Premier, Sociological Abstracts, International Bibliography of the Social Sciences, JSTOR, and Web of Science Core Collection yielded an initial dataset of 11,146 articles. Following full-text assessments and additional screening, 14 studies met inclusion criteria. Results Most studies focused on academic physicians and nurse educators, over half were conducted in North America and a predominance employed quantitative methodologies, though diverse methods were represented. Five key themes emerged as influencing retirement decisions: family considerations, financial security, health considerations, loss of professional identity, and rigid institutional structure/culture and policies. The articles were evaluated based on the MMAT's rigorous criteria, confirming that the overall quality of the included studies is acceptable and appropriate for the scope of this review. Discussion and implications While existing literature identifies key factors shaping retirement planning for academic health professionals, gaps remain in understanding effective strategies and institutional support for career transitions. Future research should study how academic health professionals transfer their skills at different career stages, compare retirement transitions among academic health professionals versus other professionals, and succession planning within academic health settings.
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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 | 0.044 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.054 | 0.041 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".