Life after Medicine: A Systematic Review of Studies of Physicians’ Adjustment to Retirement
Bibliographic record
Abstract
Background: A physician’s decision to retire has personal and social consequences. While there has been growing interest in how individuals adjust to retirement, less is known about physicians’ adjustment to retirement. Objectives: To identify and examine: 1) factors that influence how well physicians adjust to retirement; 2) reasons physicians give for retiring; and 3) advice physicians give for a successful adjustment to retirement. Methods: A systematic review of the literature was performed by searching Medline, Web of Science, Scopus, CINAHL, Ageline, Embase, Health star, ASSA, and PsycINFO databases for peer-reviewed studies published with quantitative and/or qualitative analyses of physicians’ adjustment to, satisfaction with, and/or quality of life in retirement. Two independent reviewers performed data abstraction, a quality assessment and an additional reviewer resolved inconsistencies. Content analysis was used to identify and stratify information from selected studies into themes and subthemes. Results: Based on analyses of 12 articles that met the eligibility criteria, it is evident that retirement from medicine was seen as a generally favorable phenomenon. Financial security, favorable health, engagement in activities, and psychosocial well-being were identified as key factors relevant to physician retirement adjustment. Findings suggest that physicians’ retirement transitions could be eased by a greater focus on financial planning, implementation of strategies to encourage the development of outside interests, and institutional retirement planning that honors the physician and takes place mid-career or well in advance of retirement. Conclusions: Advance planning to ensure that physicians have a strong financial situation, good health, engagement in activities outside of medicine, and positive psychosocial dynamics are likely to enhance adjustment to retirement for physicians. Future studies should account for multiple interrelating factors such as gender, changes over time, and spousal retirement to further enhance our understanding of physicians’ adjustment to retirement.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".