Health and economic consequences: How COVID-19 affected households with pet and their pets. A systematic review
Bibliographic record
Abstract
Households with pets had a unique experience with the COVID-19 since the lock-down protocols did not affect only the relationship they had with people but also with their pets. This paper analysed the evidence on the effect of COVID-19 on pets and pets owners. Employing the systematic review guidelines, the PubMed and the Google scholar database were utilised to select empirical studies published in English that focused on: (1) the COVID-19 effects on pets and (2) the COVID-19 effects on pet owners. We identified 24 articles conducted across 7 countries that met the eligibility criteria of the review. Few other studies used participants from multiple countries. Most of the studies utilised the cross-sectional survey and collected data from pet owners. Also, about 44.0% of the studies were published in only one journal (animal). COVID-19 affected the health status of both pets and pet owners. Despite the several negative health implications, there was some evidence of positive health implications. Surprisingly, several pet owners were not affected by the negative economic consequences of the pandemic. Recommendations for future studies were made in line with where attention is needed.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".