A Qualitative Exploration of Young Canadians’ Experiences of Undesired Dog Behaviours
Bibliographic record
Abstract
There is a need for research that explores the challenges associated with dog ownership. In particular, increasing our understanding of how young people manage their dog’s undesired behaviours can inform a more nuanced perspective of dog ownership as well as highlight the impact of such interactions on both dogs and young people. This qualitative study addresses this gap in the human–animal interactions literature through a secondary analysis of a data set of transcribed semi-structured interviews with seven participants aged between 17–26 years. The data were collected during a larger investigation that focused on young people’s relationships with their dogs during the COVID-19 pandemic. Using thematic analyses, we examined interviews that probed participants’ experiences and responses when their dogs misbehaved. The findings identified and explored three emerging themes including Attachment Patterns, which was supported by the codes of connection and kinship. The second theme was Synchrony and/or Lack of Synchrony, which was supported by the codes of reaction and communication. The third theme was Response to Challenging Situations, supported by the codes of emotional reactions and coping styles. Findings suggest that participants handled their dog’s undesired behaviours in unique ways and that such situations may negatively impact their emotions. Undesired dog behaviours were not related to participants’ perceptions of poor attachment patterns towards their dogs. These findings can inform efforts to foster positive interactions between young people and their dogs and reduce dog surrenders.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".