Curricula, Teaching Methods, and Success Metrics of Clinician–Scientist Training Programs: A Scoping Review
Bibliographic record
Abstract
PURPOSE: To describe the literature on clinician-scientist training programs to inform the development of contemporary and inclusive training models. METHOD: The authors conducted a scoping review, searching the PubMed/MEDLINE, CINAHL, and Embase databases from database inception until May 25, 2020. Studies presenting primary research that described and evaluated clinician-scientist training programs were identified for data abstraction. On the basis of deductive and inductive methods, information about program characteristics, curricula, teaching strategies, and success metrics was extracted. The extracted variables were analyzed using descriptive statistics. RESULTS: From the initial 7,544 citations retrieved and 4,974 unique abstracts screened, 81 studies were included. Of the 81 included studies, 65 (80.2%) were published between 2011 and 2020, 54 (66.7%) were conducted in the United States, and 64 (79.0%) described programs that provided broad clinician-scientist training. Few programs provided funding or protected research time or specifically addressed needs of trainees from underrepresented minority groups. Curricula emphasized research methods and knowledge dissemination, whereas patient-oriented research competencies were not described. Most programs incorporated aspects of mentorship and used multiple teaching strategies, such as direct and interactive instruction. Extrinsic metrics of success (e.g., research output) were dominant in reported program outcomes compared with markers of intrinsic success (e.g., career fulfillment). CONCLUSIONS: Although programs are providing clinician-scientists with practical skills training, opportunities exist for curricular and pedagogic optimization that may better support this complex career path. Training programs for clinician-scientists can address contemporary issues of wellness and equity by reconsidering metrics of program success and evolving the core tenets of their education models to include equity, diversity, and inclusion principles and patient-oriented research competencies.
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 | 0.046 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".