Assessing COVID-19 Pandemic Risk Perception and Response Preparedness in Veterinary and Animal Care Workers
Bibliographic record
Abstract
Veterinary and animal care workers perform critical functions in biosecurity and public health, yet little has been done to understand the unique needs and barriers these workers face when responding during a pandemic crisis. In this article, we evaluated the perceived risks and roles of veterinary and animal care workers during the COVID-19 pandemic and explored barriers and facilitators in their readiness, ability, and willingness to respond during a pandemic. We deployed a survey targeting US veterinary medical personnel, animal shelter and control workers, zoo and wildlife workers, and other animal care workers. Data were collected on respondents' self-reported job and demographic factors, perceptions of risk and job efficacy, and readiness, ability, and willingness to respond during the pandemic. We found that leadership roles and older age had the strongest association with decreased perceived risk and improved job efficacy and confidence, and that increased reported contact level with others (both coworkers and the public) was associated with increased perceived risk. We determined that older age and serving in leadership positions were associated with improved readiness, willingness, and ability to respond. Veterinary and animal care workers' dedication to public health response, reflected in our findings, will be imperative if more zoonotic vectors of SARS-CoV-2 arise. Response preparedness in veterinary and animal care workers can be improved by targeting younger workers not in leadership roles through support programs that focus on improving job efficacy and confidence in safety protocols. These findings can be used to target intervention and training efforts to support the most vulnerable within this critical, yet often overlooked, workforce.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".