Intervention Mapping to Develop a Print Resource for Dog-Walking Promotion in Canada
Bibliographic record
Abstract
Promoting dog walking among dog owners is consistent with One Health, which focuses on the mutual health benefits of the human-animal relationship for people and animals. In this study, we used intervention mapping (a framework to develop programs and resources for health promotion) to develop a clearer understanding of the determinants of dog walking to develop curricular and educational resources for promoting regular dog walking among dog owners. Twenty-six adult dog owners in Ontario participated in a semi-structured interview about dog walking in 2014. Thematic analysis entailing open, axial, and selective coding was conducted. Among the reasons why the participating dog owners walk their dog were the obligation to the dog, the motivation from the dog, self-efficacy, the dog's health, the owner's health, socialization, a well-behaved dog, and having a routine. The main barriers to dog walking were weather, lack of time, the dog's behavior while walking, and feeling unsafe. We compared interview results to findings in previous studies of dog walking to create a list of determinants of dog walking that we used to create a matrix of change objectives. Based on these results, we developed a print resource to promote regular dog walking among dog owners. The findings can be used by veterinary educators to inform course content that specifically educates veterinary students on the promotion of dog walking among dog owners and the benefits to both humans and animals. The study also offers veterinarians a further understanding upon which to initiate a conversation and develop educational resources for promoting regular dog walking among dog-owning clients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".