The ageing surgeon: a qualitative study of expert opinions on assuring performance and supporting safe career transitions among older surgeons
Bibliographic record
Abstract
BACKGROUND: Unlike some other safety critical professions, there is no mandatory age of retirement for doctors, including surgeons. Medical regulators in Australia are implementing additional checks on doctors from the age of 70. We describe expert opinions on assuring performance and supporting career transitions among older surgeons. METHODS: In this qualitative study, experts in four countries were purposively selected for their expertise in surgical governance. Experts responded to interviews (Australia, New Zealand and UK) or a survey (Canada). A tiered framework of interventions was developed by integrating findings with previous literature and responsive regulation theory. RESULTS: 52 experts participated. Participants valued the contribution of senior surgeons, while acknowledging that age-related changes can affect performance. Participants perceived that identity, relationships and finances influence retirement decisions. Experts were divided on the need for age-specific testing, with some favouring whole-of-career approaches to assuring safe care. A lack of validated tools for assessing performance of older surgeons was highlighted. Participants identified three options for addressing performance concerns-remediate, restrict or retire-and emphasised the need for co-ordinated and timely responses. CONCLUSION: Experts perceive the need for a staged approach to assessing the performance of older surgeons and tailoring interventions. Most older surgeons are seen to make decisions around career transitions with self-awareness and concern for patient safety. Some older surgeons may benefit from additional guidance and support from employers and professional colleges. A few poorly performing older surgeons, who are recalcitrant or lack insight, require regulatory action to protect patient safety. Developing robust processes to assess performance, remediate deficits and adjust scopes of practice could help to support safe career transitions at any age.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".