The Impact of Positive Relationships with Dogs on Students in Advanced Placement Classes
Bibliographic record
Abstract
Due to increased workload and expectations, academic stress in students has significantly risen over the past years. This study aims to look at how positive relationships with dogs affect stress reduction and academic performance among high school students enrolled in Advanced Placement (AP) classes. To conduct this study, a mixed-methods approach was used, which included quantitative surveys and qualitative interviews with 20 participants aged 14-18. All participants had to be enrolled in at least 2 AP classes. This study used the Monash Dog Ownership Relationship Scale (MDORS), which assessed emotional bonds with their dogs, and the Perceived Stress Scale (PSS), which assessed stress levels, as measures. A strong positive correlation (r = 0.89) was found between high MDORS scores and academic performance, whereas there was a weak correlation (r = -0.174) found between stress levels and academic performance. These results suggest that strong emotional bonds with dogs may lead to reduced levels of stress and increased levels of academic success. While the small sample size limits generalization, the findings support the use of therapy dog programs as a non-intrusive, cost-effective intervention in schools. Future research is needed to explore the long-term effects of strengthening the benefits of canine companionship in academic settings.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".