What is Beneficial in Our Relationships with Pets? Exploring the Psychological Factors Involved in Human–Pet Relations and Their Associations with Human Wellbeing
Bibliographic record
Abstract
Whether pet owners experience higher psychological wellbeing compared with non-pet owners remains contested. Going beyond a comparison of pet vs. non-pet owners, the current study investigated the nature of the psychological link that operates between humans and pets and tested which specific psychological factors, experienced specifically in the human–pet relationship, predict pet owners’ psychological wellbeing. The following factors were put to the test: Unconditional support and acceptance, mindfulness, and social connections with fellow humans. Data from a diverse sample of Canadian pet owners (n = 1,220) were analyzed. Mindfulness felt in the presence of one’s pet predicted more positive wellbeing on each wellbeing outcome. Perceiving that one’s pet encourages social connections with fellow humans and accepts us unconditionally also predicted more positive wellbeing on some of the wellbeing outcomes. In contrast, feeling authentic in one’s relationship with a pet predicted lower wellbeing, while perceiving that pets are less accepting of one’s negative emotions predicted higher wellbeing. Most of these associations held when accounting for the impact of these psychological factors when they are experienced in the context of human–human relations. These findings confirm the importance of investigating the nature of our psychological links to other animals, namely pets; they also provide nuances regarding the specific benefits associated with pet ownership and how the presence of pets can be beneficial to human wellness.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".