A Cross‐Sectional Survey on the Current Status and Influencing Factors of Clinical Investigators’ Competencies in Beijing, China
Bibliographic record
Abstract
Objective Clinical investigators are essential for assessing the clinical value, safety, and efficacy of innovative drugs. However, comprehensive data on their competency levels are limited in China. This study aimed to evaluate the current status and key determinants of clinical investigators’ competencies. Methods We conducted a multicenter, cross‐sectional study in 40 tertiary hospitals in Beijing in August 2023, enrolling 1397 clinical investigators. Competency was assessed via a self‐administered questionnaire, and influencing factors were identified using multivariate logistic regression. Results The median overall competency score was 103 (range: 32–160). Among 194 principal investigators (PIs), prominent competency scores ranged from 25 to 120, with a median of 93. Several factors were significantly associated with higher competency, including advanced education (doctoral degree: aOR = 1.75, 95% CI: 1.21–2.55), professional title (midlevel title: aOR = 1.77, 95% CI: 1.20–2.59; senior‐level title: aOR = 2.91, 95% CI: 1.78–4.75), experience leading clinical trials (≥ 1 trial as PI: aOR = 2.38, 95% CI: 1.69–3.36), and GCP training frequency (at least semiannually: aOR = 1.39, 95% CI: 1.07–1.81). Conclusions Efforts should target areas of underperformance, particularly by encouraging the pursuit of advanced degrees, senior professional accreditation, PI experience, and regular GCP training. Competency development requires systematic training rather than mere seniority. Consistent GCP training and adherence to international competency standards are crucial for elevating trial quality and facilitating China’s integration into global drug development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".