Three’s company? Examining the association between dog ownership and intimacy, jealousy, and satisfaction in romantic couples
Bibliographic record
Abstract
Abstract Background : Dogs in North American households are increasingly seen as integral parts of the family. Yet, limited research has been conducted exploring how dog ownership affects romantic relationships. To explore these dynamics, we examined the association of dogs with romantic relationship elements using work-family conflict, resource allocation, attachment, and family systems theories. Methods : An online cross-sectional survey was conducted with 354 adults (18+) living in the United States or Canada who currently live with a partner/spouse and have owned a dog for at least 6 months. Participants were recruited through Prolific in May 2025. The survey assessed time allocation, jealousy dynamics, intimacy, sleep impact, task division, and agreement on dog- and veterinary-related decisions. Relationship satisfaction was measured using the Dyadic Adjustment Scale (DAS). Multiple linear regressions examined predictors of dog-related associations with relationship outcomes. Results : While 69% of participants associated dog ownership with positive relationship impacts, significant challenges were also noted. Approximately 30–35% of participants reported dog care frequently diverted time from their partner. A substantial minority experienced jealousy over partner-pet cuddling (25%) and intrusion during shared activities (22%). Dogs negatively impacted sleep for 29% and sexual intercourse for 23% of participants. Women reported disproportionately handling dog care tasks. Regression analyses revealed that greater time spent on dog tasks, higher jealousy levels, and lower agreement on dog-related decisions were significantly associated with lower relationship satisfaction. Conclusions : Dogs introduce complex dynamics into romantic partnerships that parallel, yet differ from, challenges associated with human children. While predominantly positive, successful dog integration requires proactive communication, realistic expectations, and equitable task distribution. These findings highlight the potential value of pre-adoption counseling and clinical interventions for addressing pet-related relationship dynamics. Results should be interpreted with caution, however, given the study’s reliance on a convenience sample, the use of unvalidated assessment instruments, and subjective reporting from only one partner.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".