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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".