Perspectives, Experiences, and Opportunities of Academic Surgeons in the Era of Health Care Corporatization
Bibliographic record
Abstract
OBJECTIVE: To characterize contemporary surgeons' viewpoints and perspectives on the academic mission during health care corporatization. BACKGROUND: Academic surgery, traditionally driven by the tripartite missions of excellence in clinical care, scientific research, and education, faces increasing challenges from a corporatized health care environment. While previous studies have addressed the financial aspects of corporatization, a comprehensive evaluation of academic surgeons' attitudes and experiences remains lacking. METHODS: An online survey was distributed to a national sample of academic surgical department chairs and program directors assessing their views, perspectives, and experiences with health care corporatization. We also analyzed trends in National Institutes of Health funding and abstract submissions to the Academic Surgical Congress. RESULTS: Of 105 respondents, 72.9% believe corporatization negatively impacts academic surgery. Among program directors, 79.1% feel corporatization emphasizes clinical productivity over research and teaching. Similarly, 82.8% of department chairs report their faculty often must prioritize clinical duties over other academic responsibilities.Qualitative analysis reveals that surgeons perceive corporatization as a significant barrier to achieving their goals. Many feel that profit has become "king," reducing their roles to "cogs in a wheel."While overall National Institutes of Health funding increased, the proportion awarded to surgical departments remained largely unchanged from 2006 to 2023 (2.7% vs 2.8%, P = 0.94). In addition, the proportion of basic science abstracts submitted to the Academic Surgical Congress declined by 37.9%. CONCLUSIONS: Corporatization has had a profound impact on surgeons' perceptions of their roles, responsibilities, and autonomy. Surgical departments must take intentional steps to preserve the multidimensional mission of the academic surgeon.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".