Relationships’ Best Friend: Links between Pet Ownership, Empathy, and Romantic Relationship Outcomes
Bibliographic record
Abstract
The benefits of pets on individual wellbeing is well established. But can pets also have benefits for romantic relationships? Using mixed methods, three studies explored the link between pet ownership and romantic relationship quality. First, using a grounded theory approach, we qualitatively investigated participants’ personal beliefs of how their pets influence their romantic relationships by coding open-ended responses. Results suggested that pets are seen as having predominantly positive (86.5%) effects, followed by few neutral (8%) and negative (4.5%) effects (study 1). We next compared a community sample of pet owners’ reports of relationship quality with those of non-pet owners. Results suggested that pet ownership was associated with several relationship benefits (greater overall relationship quality, partner responsiveness, adjustment, and relational investment) compared with couples without pets (study 2). Finally, we examined one possible reason for why pets may benefit relationships: A pet might provide the opportunity to practice empathic abilities, which is a crucial ability in the maintenance of positive relationships. Results showed that the number of years an individual owned a pet was positively correlated with empathic concern, which in turn was linked to several relationship benefits (commitment, couple identity, and relationship maintenance behaviors; study 3). In sum, three studies provided initial evidence that there is indeed a positive association between two important relationships in peoples’ lives: their partners and their pets.
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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 | 0.016 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".