Understanding action control of daily walking behavior among dog owners: a community survey
Bibliographic record
Abstract
BACKGROUND: Walking among dog owners may be a means to achieve health benefits, yet almost half of owners (approximately 30% of households) are not regularly walking their dogs. Current research on the correlates of dog walking has generally considered intention as the primary determinant of behavior, yet the intention-behavior relationship is modest. The purpose of this paper was to apply a framework designed to evaluate the intention-behavior gap, known as multi-process action control (M-PAC), to understand daily walking among dog owners. METHOD: A community sample of adult dog owners (N = 227) in Victoria, Canada completed M-PAC measures of motivational (dog and human outcome expectations, affective judgments, perceived capability and opportunity), regulatory (planning), and reflexive (automaticity, identity) processes as well as intention to walk and behavior. RESULTS: Three intention-behavior profiles emerged: a) non-intenders who were not active (26%; n = 59), b) unsuccessful intenders who failed to enact their positive intentions (33%; n = 75), and c) successful intenders who were active (40%; n = 91). Congruent with M-PAC, a discriminant function analysis showed that affective judgements (r = 0.33), automaticity (r = 0.38), and planning (r = 0.33) distinguished between all three intention-behavior profiles, while identity (r = 0.22) and dog breed size (r = 0.28) differentiated between successful and unsuccessful intenders. CONCLUSIONS: The majority of dog owners have positive intentions to walk, yet almost half fail to meet these intentions. Interventions focused on affective judgments (e.g., more enjoyable places to walk), behavioral regulation (e.g., setting a concrete plan), habit (e.g., making routines and cues) and identity formation (e.g., affirmations of commitment) may help overcome difficulties with translating these intentions into action, thus increasing overall levels of walking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".