Stakeholder perception of quality management of investigator-initiated clinical trials
Bibliographic record
Abstract
BACKGROUND: Investigator-initiated clinical trials have flourished in China. However, research on stakeholders’ perceptions and management capacity of investigator-initiated clinical trials, which are crucial for optimizing resource utilization, timely identifying barriers affecting high-quality Investigator-initiated clinical trials output, and providing evidence for establishing, improving, and evaluating the quality management system for Investigator-initiated clinical trials, is rare. Our objective is to assess the perception of the standardized quality management of Investigator-initiated clinical trials among stakeholders at different levels of healthcare institutions. METHODS: We conducted a cross-sectional study using an electronic survey designed using REDCap conducted between June and September 2024 in Healthcare institutions of various levels in Beijing, China. Hospital level, education level, years in research, familiarity, distinction ability, medical personnel, research administrators, methodologists, and other personnel were investigated as potential influencing factors. The primary outcomes were the total number of correct answers and weighted correct rates for each domain and all six domains. RESULTS: A total of 717 individuals participated in the study. The distribution of total correct answers and weighted correct rates across all domains was 20.0 (15.0–23.0) and 0.8 (0.6–0.9), respectively. Participants from higher-level hospitals, those with higher education levels, greater familiarity, and higher distinction ability, and administrators performed significantly better in terms of the total number of correct answers and weighted correct rates across all domains. CONCLUSIONS: The stakeholders performed well overall. However, much room for improvement still exists. Hospital level, education level, familiarity, distinction ability, and the role of research administrators can influence the overall performance of stakeholders. Establishing, improving, and evaluating a quality management system for Investigator-initiated clinical trials in China is crucial. TRIAL REGISTRATION: Not applicable.
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 | 0.247 | 0.855 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 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".