My ‘Perfect’ Dog: Undesired Dog Behaviours and Owners’ Coping Styles
Bibliographic record
Abstract
Abstract Research continues to shed light on the benefits associated with dog ownership. However, we know virtually nothing about how undesired dog behaviours impact young people’s behavioural and perceptive experiences with their dogs. Understanding these impacts is important because it can offer a more nuanced understanding of the effects of dog ownership. This exploratory study applied a qualitative methodology to examine young people’s perceptions about their dog’s behavioural issues, along with a focus on young people’s coping styles. Seven participants aged between 17 and 26 years engaged in semi-structured in-depth one-on-one interviews about their experiences with their dogs and their coping strategies in response to undesired dog behaviours. Thematic coding identified the following salient themes in participants’ responses which were subsequently explored in detail: (1) severity of dogs’ behavioural issues, (2) participants’ coping styles, (3) the relationship between the dog’s behavioural issues and the participant, (4) participants’ emotional reactions, (5) participants’ self-evaluation – insights – of their coping styles, and (6) similarities between participants’ coping styles with humans to whom they are close and those used with their dogs. Findings from this study indicate that young people prefer proactive coping styles when coping with undesired behaviours in their dogs. This study’s findings also indicate that the severity of dogs’ behavioural issues is associated with young people’s emotions (e.g., more severe behavioural issues seem to elicit more intense emotions). The significance of these findings for practice in the field of human-animal interactions is discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".