Clinician-researcher’s perspectives on clinical research during the COVID19 pandemic
Bibliographic record
Abstract
OBJECTIVES: The outcome of well-performed clinical research is essential for evidence-based patient management during pandemics. However, conducting clinical research amidst a pandemic requires researchers to balance clinical and research demands. We seek to understand the values, experiences, and beliefs of physicians working at the onset of the COVID-19 pandemic in order to inform clinical research planning. We aim to understand whether pandemic settings affect physician comfort with research practices, and how physician experiences shape their understanding of research in a pandemic setting. METHODS: A survey tool was adapted to evaluate familiarity and comfort with research during a pandemic. A cross-sectional, online questionnaire was distributed across Canadian research networks early in the COVID-19 outbreak. The survey was administered between March 11th and 17th, 2020, during a time of local transmission but prior to the surge of cases. We aimed to recruit into the survey physicians in infectious disease and critical care research networks across Canada. RESULTS: Of the 133 physician respondents, 131 (98%) considered it important to conduct clinical research during the COVID-19 pandemic. Respondents were more accepting of adaptations to the research process in during a pandemic compared to in a non-pandemic setting, including conducting research with deferred consent (χ2 = 8.941, 95% CI: -0.264, -0.085, p = 0.003), using non-identifiable observational data with a waiver of consent with a median score of 97 out of 100 (IQR: 79.25-100) vs median 87 out of 100 (IQR: 63-79) (95% CI: -12.43, 0.054, p = 0.052). The majority felt that research quality is not compromised during pandemics. CONCLUSIONS: Physicians consider it important to conduct research during a pandemic, highlighting the need to expedite research activities in pandemic settings. Respondents were more accepting of adaptations to the research process for research conducted during a pandemic, compared to that conducted in its absence of a pandemic.
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 | 0.199 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.040 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier 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".