Animal Welfare within the Human-Dog Dyad: The Relationship between Human Mental Health and Pet Dog Problem Behaviours.
Bibliographic record
Abstract
Pet ownership interventions reduce owner stress and increase overall quality of life, and as such, pet ownership is sometimes considered as a method of treatment. However, to date research on the human-animal bond and pet ownership has primarily focused on the benefits for the human, and has paid little attention to welfare of the animal. From a caregiver perspective, the owner-pet dyad is analogous to the parent-child dyad. Studies on the parent-child dyad show that children are more likely to display stress-related behaviours if raised by parents who suffer from depression or anxiety, possibly due to social-modeling and the impact the environment has on learned behaviours. Additionally, parental depression is associated with child neglect. The purpose of this study is to see if these correlations also exists among the owner-pet dyad. Using the Inventory of Anxiety and Depression Symptoms (IDAS) and the Canine Behavioral Assessment and Research Questionnaire (C-BARQ), we will measure university students’ level of anxiety and depression, and problem behaviours in their pet dog. Our expected findings are positive correlations between level of owner depression and anxiety, and problem behaviours, typical of stress, in the owner’s pet dog. Furthermore, we expect to find negative correlations between owner depression and owner level of pet dog care. These findings will help to better understand the human-dog dyad and assist clinicians in making informed, ethical decisions while promoting live-in-animal-assisted interventions. Discipline: Psychology Honours Faculty Mentor: Dr. Eric Legge
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".