Did the COVID-19 Pandemic Spark a Public Interest in Pet Adoption?
Bibliographic record
Abstract
This study aimed to determine if there has been an increase of global interest on pet adoption immediately after the WHO declaration of the pandemic and if the effect has been sustainable in 8 months on. We conducted a Google Trends search using keywords related to pet adoption. Relative search volume (RSV) was scored between 0 and 100 for the lowest and the highest, respectively. Top countries contributing to the dataset included Australia, the United States, Canada, New Zealand, the United Kingdom, Singapore, the Philippines, and Malaysia. From 2015 through 2020, the worldwide RSV for the categories of pet, dog and cat adoption peaked between April and May 2020, the early epidemic phase of the pandemic. These were significantly higher than the 5-year worldwide average RSV for all three categories ( P = 0.001). Comparing to the same period in 2019, the RSV ratio (2020/2019) for both dog and cat adoption increased by up to 250%. Nonetheless, the RSV for dog adoption has been decreasing since July 2020 and returned to the 5-year average by December 2020. In contrast, the interest in cat adoption remained sustainably high, possibly reflecting the feline acclimation to indoor living. In conclusion, the global interest in pet adoptions surged in the early phase of the pandemic but not sustainable. With the launch of COVID-19 vaccines, there is a concern for separation anxiety and possible abandonment of these newly adopted pets when the owners would leave their homes for work in the future.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".