The Initial Months of COVID-19: Dog Owners' Veterinary-Related Concerns
Bibliographic record
Abstract
Veterinarians, like many other professions, were significantly impacted by the onset of COVID-19 in the spring of 2020. Standard practices were disrupted, and veterinary hospitals had to quickly modify standard protocols to safely serve their clients and patients. The purpose of this study was to better understand dog owners' fears and concerns pertaining to veterinary care and obtainment of pet care products and food during the lock down phase of a pandemic to be better prepared to address these concerns now and in the future. To this end, an online, anonymous, cross-sectional survey was designed and distributed to adult dog owners via social media. The results, from a total of 4,105 participants (the majority from the United States and Canada), indicated substantial areas of concern. The number one concern of dog owners during this time was the availability of emergency veterinary care. Owners under 30 years of age, compared to older owners, were significantly more concerned about both availability and cost of veterinary care (emergency and non-emergency). The ability to care for one's dog if they were to become ill was a concern for many owners, yet only 60% had identified a caretaker for their dog if one was needed. These results suggest that the majority of dog owners remained true steadfast guardians of their dogs, continuing to make them a priority, even during pandemic times. Suggestions to help mitigate dog owners' concerns and improve communication between owners and veterinarian teams are offered.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".