A systematic review of physician retirement planning
Bibliographic record
Abstract
BACKGROUND: Physician retirement planning and timing have important implications for patients, hospitals, and healthcare systems. Unplanned early or late physician retirement can have dire consequences in terms of both patient safety and human resource allocations. This systematic review examined existing evidence on the timing and process of retirement of physicians. Four questions were addressed: (1) When do physicians retire? (2) Why do some physicians retire early? (3) Why do some physicians delay their retirement? (4) What strategies facilitate physician retention and/or retirement planning? METHODS: English-language studies were searched in electronic databases MEDLINE, Web of Science, Scopus, CINAHL, AgeLine, Embase, HealthSTAR, ASSA, and PsycINFO, from inception up to and including March 2016. Included studies were peer-reviewed primary journal articles with quantitative and/or qualitative analyses of physicians' plans for, and opinions about, retirement. Three reviewers independently assessed each study for methodological quality using the Newcastle-Ottawa Scale for quantitative studies and Critical Appraisal Tool for qualitative studies, and a fourth reviewer resolved inconsistencies. RESULTS: In all, 65 studies were included and analyzed, of which the majority were cross-sectional in design. Qualitative studies were found to be methodologically strong, with credible results deemed relevant to practice. The majority of quantitative studies had adequate sample representativeness, had justified and satisfactory sample size, used appropriate statistical tests, and collected primary data by self-reported survey methods. Physicians commonly reported retiring between 60 and 69 years of age. Excessive workload and burnout were frequently cited reasons for early retirement. Ongoing financial obligations delayed retirement, while strategies to mitigate career dissatisfaction, workplace frustration, and workload pressure supported continuing practice. CONCLUSIONS: Knowledge of when physicians plan to retire and how they can transition out of practice has been shown to aid succession planning. Healthcare organizations might consider promoting retirement mentorship programs, resource toolkits, education sessions, and guidance around financial planning for physicians throughout their careers, as well as creating post-retirement opportunities that maintain institutional ties through teaching, mentoring, and peer support.
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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 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".