Dog ownership, dog walking, and social capital
Bibliographic record
Abstract
Abstract Evidence for social pathways to health benefits for dog owners appears positive but less well-developed. Our study aimed to estimate the differences in social capital by dog ownership and dog walking status among young-to-middle-aged adults and older adults in Japan. Data from 3606 residents living in Japan were used. Data on social capital, dog ownership, and dog walking were collected by questionnaires. Age-stratified multivariable linear regression models were used to estimate differences in social capital scores by dog ownership and dog walking status. Among young-to-middle-aged adults, the mean of the activities with neighbours score, adjusted for covariates, was significantly higher (p < 0.05) for the dog owner walkers group compared to the non-dog owners group. Among older adults, no significant differences in the marginal means of social capital scores were observed between the three groups of non-dog owners, dog owner non-walkers, and dog owner walkers. While the benefits of social capital for a healthy lifestyle have been well-documented, few means have been identified to intervene in social capital. Building on and expanding the known health benefits of dog ownership and dog walking, this study revealed modest support for the link between dog walking and activities with neighbours among young-to-middle-aged adults, but no meaningful associations were found for older adults. Additionally, no significant link was observed between dog walking and social cohesion among either age group. Future research can further improve the use of dog-based behavioural health interventions for fostering social capital.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".