Strategies for Supporting Physician–Scientists in Faculty Roles: A Narrative Review With Key Informant Consultations
Bibliographic record
Abstract
PURPOSE: Physician-scientists are a population in decline globally. Solutions to reverse this decline often have focused on the training pipeline. Less attention has been paid to reducing attrition post training, when physician-scientists take up faculty roles. However, this period is a known time of vulnerability because of the pressures of clinical duties and the long timeline to securing independent research funding. This narrative review explored existing knowledge regarding how best to support physician-scientists for success in their faculty roles. METHOD: The authors searched the Medline, Embase, ERIC, and Cochrane Library databases for articles published from 2000 to 2016 on this topic and interviewed key informants in 2015 to solicit their input on the review results. RESULTS: The authors reviewed 78 articles and interviewed 16 key informants. From the literature, they developed a framework of organizational (facilitate mentorship, foster community, value the physician-scientist role, minimize financial barriers) and individual (develop professional and research skills) strategies for supporting physician-scientists. They also outlined key knowledge gaps representing topics either rarely or never addressed in the reviewed articles (percent research time, structural hypocrisy, objective assessment, group metrics, professional identity). The key informants confirmed the identified strategies and discussed how the gaps were particularly important and impactful. CONCLUSIONS: This framework offers a basis for assessing an organization's existing support strategies, identifying outstanding needs, and developing targeted programming. The identified gaps require attention, as they threaten to undermine the benefits of existing support strategies.
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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 | 0.027 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".