Principal investigators’ experience of COVID-19 therapeutic clinical trials in Japan: a qualitative study
Bibliographic record
Abstract
Objective During the COVID-19 pandemic, many clinical trials were conducted to identify effective COVID-19 therapeutics. However, while a large amount of resources was invested and significant numbers of patients participated, this did not necessarily have an impact on clinical practice. To face these issues, initiatives such as the 100 Days Mission have been set out globally. Yet, limited data exist on the context surrounding the implementation of clinical trials at a national level during a health emergency. The study explored experiences and perceptions of principal investigators in conducting clinical trials for COVID-19 therapeutics in Japan. Design A qualitative study was conducted using semistructured interviews. The obtained data were inductively analysed using thematic analysis. Setting and participants We interviewed 15 principal investigators between September and November 2022 who conducted investigator-initiated clinical trials on the development of COVID-19 therapeutics in Japan. Results Three themes were generated: structural barriers , fragmented efforts and limited evidence generation . Structural barriers and fragmented efforts comprised four subthemes: individual, institutional, interinstitutional and policy/regulatory levels. Structural barriers at all levels included (1) limitations of individual capabilities, (2) the double burden of clinical practice and research, (3) inefficient interinstitutional collaboration and (4) regulatory frameworks and available resources that interrupt stakeholders’ actions, leading to limited evidence generation despite the fragmented efforts of principal investigators and other stakeholders. Conclusions This study illustrated that the efforts of Japanese principal investigators did not necessarily pay off in identifying therapeutics. A strategic and systematic approach for an improved national clinical trial ecosystem must be sought during the interpandemic period to overcome structural barriers in harmonisation with the global stakeholders.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".