Determinants of research productivity during postgraduate medical education: a structured review
Bibliographic record
Abstract
BACKGROUND: Although formal participation in research is an integral and often mandatory component of clinical training programs, resulting productivity is highly variable. The objective of this review was to identify determinants of successful research performance among graduate medical education trainees. METHODS: A structured review of the published literature was performed by searching PubMed, CINAHL, and EMBASE from inception through to 7 April, 2021. Articles examining graduate medical education trainee research productivity evidenced by publications in peer-reviewed journals were included. RESULTS: Eighty-five articles were included of which most (66; 78%) were reported from the USA or Canada (10; 12%). A wide range of disciplines were represented with the most common being general surgery, internal medicine, orthopedic surgery, and pediatrics. Themes (number of reports) included trainee characteristics (n = 24), project characteristics (n = 8), mentoring/supervision (n = 11), and programmatic aspects (n = 57). Although variable results were observed, research productivity tended to be higher with prior research experience, later years of training, male gender, and pursuit of a postgraduate degree. Few project related aspects of success were identified. Trainee publication was associated with mentors with higher rank, publication productivity, and supportive academic environments. Training programs with organised programs/curricula including protection of time for research were associated with increased productivity as were provision of incentives or rewards but not mandatory requirements. CONCLUSION: This review identifies several trainee characteristics, project and mentor aspects, and programmatic aspects associated with increased productivity that may serve as a useful resource for trainees and graduate medical education training programs.
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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 | 0.024 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".