Unleashing their potential: a critical realist scoping review of the influence of dogs on physical activity for dog-owners and non-owners
Bibliographic record
Abstract
BACKGROUND: Dog-owners tend to be more physically active than non-owners; however, dogs have also been shown to inhibit physical activity for non-owners, under some circumstances. METHODS: We conducted a scoping review to identify studies pertaining to the influence of dogs on physical activity for both dog-owners and non-owners, and adopted a critical realist orientation to draw inferences about the positive and negative impact of dogs via their affect on physical and social environments. RESULTS: We identified 35 studies from disparate literatures for review. These studies confirm that dog and owner behaviors affect shared physical and social environments in ways that may influence physical activity patterns, not only among dog-owners but also among non-owners. The direction of influence appears to be most positive in neighborhoods exhibiting high levels of social cohesion, socioeconomic status, perceived safety, dominant culture, or all of these. In disadvantaged neighborhoods, the health of women as well as older adults may be disproportionately affected by dog and owner behavior. CONCLUSIONS: While dogs have the potential to increase physical activity for both dog-owners and non-owners, the presence or absence of dogs will not have a standard effect across the physical and social environments of all neighborhoods. Dogs' contributions to shared environments in ways that support physical activity for all must be leveraged. Thus, specific contextual factors must be considered in relation to dogs when planning neighborhood-level interventions designed to support physical activity. We suggest this population health topic merits further investigation.
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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 | 0.077 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".