Characteristics of Biostatistics, Epidemiology, and Research Design Programs in Institutions With Clinical and Translational Science Awards
Bibliographic record
Abstract
PURPOSE: To learn the size, composition, and scholarly output of biostatistics, epidemiology, and research design (BERD) units in U.S. academic health centers (AHCs). METHOD: Each year for four years, the authors surveyed all BERD units in U.S. AHCs that were members of the Clinical and Translational Science Award (CTSA) Consortium. In 2010, 46 BERD units were surveyed; in 2011, 55; in 2012, 60; and in 2013, 61. RESULTS: Response rates to the 2010, 2011, 2012, and 2013 surveys were 93.5%, 98.2%, 98.3%, and 86.9%, respectively. Overall, the size of BERD units ranged from 3 to 86 individuals. The median FTE in BERD units remained similar and ranged from 3.0 to 3.5 FTEs over the years. BERD units reported more availability of doctoral-level biostatisticians than doctoral-level epidemiologists. In 2011, 2012, and 2013, more than a third of BERD units provided consulting support on 101 to 200 projects. A majority of BERD units reported that between 25% and 75% (in 2011) and 31% to 70% (in 2012) of their consulting was to junior investigators. More than two-thirds of BERD units reported their contributions to the submission of 20 or more non-BERD grant or contract applications annually. Nearly half of BERD units reported 1 to 10 manuscripts submitted annually with a BERD practitioner as the first or corresponding author. CONCLUSIONS: The findings regarding BERD units provide a benchmark against which to compare BERD resources and may be particularly useful for institutions planning to develop new units to support programs such as the CTSA.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".