“And Then COVID-19 Happened”: Impacts of the Pandemic on Hazard and Disaster Researchers
Bibliographic record
Abstract
There is a limited but growing body of literature on the impacts of the COVID-19 pandemic on academic faculty, scholars, and researchers in the US and around the world. In this article, we present findings of a US-based study conceived in 2020 at the onset of COVID-19. In it, we focus on ways in which the pandemic has affected the professional and personal lives of hazard and disaster researchers from the social sciences, public health, engineering, and other fields who started to examine social dimensions of COVID-19 at the beginning of the pandemic. Data are drawn from a systematic, qualitative, longitudinal study in which we gathered data at two points in time across approximately 18 months between the summer of 2020 and spring of 2022. Thirty interviewees in the first phase of the study and 18 interviewees in the second phase shared their experiences navigating the challenges of conducting pandemic-related research while themselves working in the pandemic environment. Through their rich narratives, study participants provided a range of perspectives. With respect to their professional lives, they described issues associated with social isolation, working from home, using digital platforms to conduct research and business, and shifts to online teaching, among other things. At a personal level, they discussed challenges of childcare and caregiving, as well as living in a generally stressful environment. We conclude by offering a number of suggestions for policy-makers and decision-makers to consider in the event of future events such as COVID-19.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.026 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".