Building Career Paths for Ph.D., Basic and Translational Scientists in Clinical Departments in the United States: An Official American Thoracic Society Workshop Report
Bibliographic record
Abstract
Abstract Rationale To identify barriers and opportunities for Ph.D., basic and translational scientists to be fully integrated into clinical units. Objectives In 2022, an ad hoc committee of the American Thoracic Society developed a project proposal and workshop to identify opportunities and barriers for scientists who do not practice medicine to develop successful careers and achieve tenure-track faculty positions in clinical departments and divisions within academic medical centers (AMCs) in the United States. Methods This document focuses on results from a survey of adult and pediatric pulmonary, critical care, and sleep medicine division chiefs as well as a survey of workshop participants, including faculty in departmental and school leadership roles in both basic science and clinical units within U.S. AMCs. Results We conclude that full integration of non-clinically practicing basic and translational scientists into the clinical units, in addition to their traditional placements in basic science units, best serves the tripartite mission of AMCs to provide care, perform research, and educate the next generation. Evidence suggests clinical units do employ Ph.D. scientists in large numbers, but these faculty are often hired into non-tenure track positions, which do not provide the salary support, start-up funds, research independence, or space often associated with hiring in basic science units within the same institution. These barriers to success of Ph.D. faculty in clinical units are largely financial. Conclusions Our recommendation is for AMCs to consider and explore some of our proposed strategies to accomplish the goal of integrating basic and translational scientists into clinical units in a meaningful way.
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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 | 0.066 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".