One Health, the Human-Animal Bond and Well-Being During the Covid 19 Pandemic
Bibliographic record
Abstract
Using a One Health lens, this study explored whether the strength of the bond between humans and non-human animals would predict well-being during the COVID-19 pandemic. Based on the substantial existing research done over the last several decades, we hypothesized that the presence of non-human animals (NHAs) may be linked directionally to well-being. Participants were recruited to this online survey using social media. A demographic survey as well as the World Health Organizations’ Well-being Scale (WHO5) and 10-item Pet Attachment Scale (PAS) were used. Results showed that the human-animal bond, as measured by the 10-item PAS, was the only significant predictor of well-being. The bond with NHAs itself was influenced by the role non-human animals play, with the strongest bond among those who reported that they considered NHAs to be family members. The article concludes that preserving and supporting the human-animal bond during stressful and dangerous times, such as a pandemic, is an important mental and physical health protective strategy that governments should support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".