Critical Gaps in Understanding the Clinician–Scientist Workforce: Results of an International Expert Meeting
Bibliographic record
Abstract
Clinician-scientists-health care professionals expert in research and clinical practice-can play a vital role in translating research outcomes to clinical practice. Concerns about the sustainability of the clinician-scientist workforce have been expressed in the literature for decades. Although many have made recommendations to increase the clinician-scientist workforce, there has been no substantial change. Therefore, an international expert meeting was held in March 2017 in Utrecht, the Netherlands, with the goal of discovering unidentified gaps in our understanding of challenges to the sustainability of the clinician-scientist workforce. Nineteen individuals (steering committee members; representatives from the AAMC, AFMC, and RCPSC; and physician-scientists, nurse-scientists, education scientists, deans, vice deans, undergraduate and postgraduate program directors, and a medical student) from Canada, the Netherlands, the United States, and Singapore participated in the meeting. The meeting identified 3 critical questions to be addressed: (1) What is the particular nature of the clinician-scientist role? (2) How are clinician-scientists to be recognized within the health and health research ecosystem? and (3) How can the value that clinician-scientists add to translational medicine and research be clarified to stakeholders and the public? The meeting participants identified a 3-fold agenda to address these questions: articulating the value proposition of clinician-scientists, supporting professionalization and professional identity development, and integrating clinical and research training. Addressing the 3 critical questions will likely contribute to a wider recognition of the value of clinician-scientists and be a first step in advancing from recommendations toward system-level changes to reinvigorate the clinician-scientist workforce.
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 | 0.008 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".