Beyond companionship: psycho-social benefits of pet ownership
Bibliographic record
Abstract
BACKGROUND: Pet ownership, which has increased over the last decade, can offer owners health benefits. However, relatively few studies have examined the psycho-social benefits of pet ownership, particularly across diverse contexts and types of pets. Existing research is largely Western-focused, and has paid limited attention to the potential interrelationships between pet ownership, pet types, and gender. This study planned to examine associations between various categories of pet ownership and multiple psycho-social benefits in a sample of adults from Japan. METHOD: A cross-sectional design was applied to investigate and analyze pet species and psycho-social health outcomes. Data were collected via an online survey conducted in 21 major Japanese cities during October to November 2020. Four pet ownership categories were analyzed: "non-pet", "dog", "other pet", and "dog and other pet". Gender-stratified multivariable linear regression models were applied to explore the associations between psycho-social health outcomes and pet ownership categories for both men and women. RESULTS: A total of 8,821 participants were included in analysis. The results showed owning both dogs and other pets was associated with higher neighborhood place attachment and social capital for both men and women. For men, owning other pets (excluding dogs) was positively associated with higher neighborhood place attachment and social capital. Dog ownership was also positively associated with social capital, but not neighborhood place attachment, regardless of gender. CONCLUSIONS: The findings highlight the psycho-social benefits of both single-dog and multi-pet ownership, suggesting their potential for fostering health and social well-being. More research is needed to examine the contributions of specific multi-pet and single-dog ownership and the pathways by which pet ownership contribute to health and well-being.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".