Hospital Preparedness for Conducting Clinical Research During a Pandemic: A Nationwide Survey Among Designated Medical Institutions for Infectious Diseases in Japan
Bibliographic record
Abstract
In Japan, the Infectious Disease Control Law designates certain institutions across the country as medical institutions for infectious diseases, with the role to respond to and prepare for epidemic or pandemic infections. Since the early stages of the COVID-19 pandemic, these designated medical institutions have provided clinical care to patients with COVID-19. While these institutions primarily handle clinical care, they are also well poised to conduct rigorous clinical research that is needed to address future health emergencies. The COVID-19 pandemic highlighted the importance of clinical research as a medical countermeasure through its role in the development of effective novel vaccines and therapeutics. Under the Japanese system, designated medical institutions that cared for patients with COVID-19 had the privilege to access the earliest cases and were uniquely positioned to contribute to scientific evidence. Based on this understanding, we conducted a nationwide survey and analyzed data from 100 designated medical institutions to better understand their experiences and involvement in clinical research during the COVID-19 pandemic and their readiness and willingness to conduct clinical research in a future health emergency. While quite a few institutions showed willingness to participate in infectious disease research in the event of a future health emergency, it was evident that many would require additional expertise and financial support to facilitate such research. Our analysis suggests that further capacity development, empowerment for clinical research, and a strong collaborative network across stakeholders are required to improve pandemic response and preparedness in Japan.
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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".