Planning for retirement from medicine: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Evidence suggests there are important personal and social consequences associated with inadequate retirement planning for physicians. We evaluated whether academic physicians felt satisfied with their retirement planning, and identified obstacles to retirement planning and a set of factors to facilitate retirement planning. METHODS: We applied a sequential mixed-methods research design to explore and examine factors that facilitate academic physician retirement planning using data collected from multiple sources (including 7 focus groups, an internet-based survey and 23 in-depth interviews). We examined survey results regarding retirement planning satisfaction and preferences for complete versus gradual retirement. We used thematic analysis to examine verbatim transcripts and notes from the focus groups and interviews. RESULTS: Survey data (response rate 51%) indicated that 10% of respondents were very satisfied with their retirement planning and 89.5% would prefer to retire gradually rather than stop work completely. Key barriers to retirement planning that emerged included poor personal financial management, rigid institutional structures and professional norms. Facilitators included financial planning resources for physicians at multiple career stages, opportunities and resources for later-career transitions and later-career mentorship support for intergenerational collaboration, and recognition of retirees. INTERPRETATION: Key findings highlight perceived barriers to retirement planning at various career stages in addition to factors that can enhance physicians' retirement planning, including creating gradual and flexible retirement options, supporting ongoing discussions about financial planning and later career transitions, and fostering a culture that continues to honour and involve retirees. Medical institutions could foster innovative models for later-career transitions from medicine in ways that address physicians' needs at various career stages, support gradual transitions from practice and recognize the value of experienced, capable later-career physicians and retirees.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 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) | 0.006 | 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".