Dogs Have Love Medicine: A Case Study Exploration of the Dog-Human Interface in a Remote Saskatchewan Community
Bibliographic record
Abstract
Many northern, remote and Indigenous communities in Canada report challenges with care and control of dog health, welfare and populations. Concurrently, these communities typically experience inadequate access to health and welfare services for dogs. To support optimal outcomes at the dog-human interface, we sought to understand the contributors to and determinants of these outcomes by understanding the dog-human relationship and the strengths and opportunities in current approaches to dog care and control in one northern Saskatchewan region. \nA team of researchers, service providers, decision makers and community members conducted a decolonizing qualitative case study incorporating Indigenous methodologies. A community-oriented approach was taken to ensure community priorities and empowerment were the focus. Data sources included 20 conversational interviews; 9 reflective journals from high-school students; 9 key informant interviews; and documents including relevant legislation; local dog bylaws; and local humane society records. Collaborative thematic and content analyses were conducted to derive themes. Rigor was maintained through member checking, data triangulation, team reflexivity and generation of usable outcomes for the study community. \nFindings reveal multi-dimensional dog-human relationships illustrated by an extended medicine wheel model where each domain (physical, social, emotional and spiritual) represents an element of relational health. The model allows consideration of the needs for a healthy relationship, and the proximal, intermediate and distal determinants of dog care and control. In this region, dog care and control concerns are mediated by inadequate access to resources and support for dog health and welfare. Dog health and welfare engages multiple sectors, and gaps in dog care and control are determined by local, provincial, and holistic or systemic factors. \nChallenges at the dog-human interface require local, regional and systemic capacity-building. Balanced relational health can be achieved by attending to factors that bolster each domain. The needs of dogs must be met at individual and community levels to ensure dog health and welfare, and this will require creative changes to practice and policy. Holistic influences on outcomes between dogs and humans draw attention to sociopolitical contexts and stakeholder approaches, providing opportunities for improved relational health overall.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".