Career Needs Assessment for Early Career Academic Surgeons Using a Modified Accelerated Delphi Process
Bibliographic record
Abstract
INTRODUCTION: Over the past 2 decades, physicians' wellbeing has become a topic of interest. It is currently unclear what the current needs are of early career academic surgeons (ECAS). METHODS: Consensus statements on academic needs were developed during a Delphi process, including all presenters from the previous European Surgical Association (ESA) meetings (2018-2022). The Delphi involved (1) a literature review, (2) Delphi form generation, and (3) an accelerated Delphi process. The Delphi form was generated by a steering group that discussed findings identified within the literature. The modified accelerated e-consensus approach included 3 rounds over a 4-week period. Consensus was defined as >80% agreement in any round. RESULTS: Forty respondents completed all 3 rounds of the Delphi. Median age was 37 years (interquartile range 5), and 53% were female. Majority were consultant/attending (52.5%), followed by PhD (22.5%), fellowship (15%), and residency (10%). ECAS was defined as a surgeon in 'development' years of clinical and academic practice relative to their career goals (87.9% agreement). Access to split academic and clinical contracts is desirable (87.5%). Consensus on the factors contributing to ECAS underperformance included: burnout (94.6%), lack of funding (80%), lack of mentorship (80%), and excessive clinical commitments (80%). Desirable factors to support ECAS development included: access to e-learning (90.9%), face-to-face networking opportunities (95%), support for research team development (100%), and specific formal mentorship (93.9%). CONCLUSION: The evolving role and responsibilities of ECAS require increasing strategic support, mentorship, and guidance on structured career planning. This will facilitate workforce sustainability in academic surgery in the future.
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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 | 0.105 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".