Petting away pre‐exam stress: The effect of therapy dog sessions on student well‐being
Bibliographic record
Abstract
Recently, many universities have implemented programmes in which therapy dogs and their handlers visit college campuses. Despite the immense popularity of therapy dog sessions, few randomized studies have empirically tested the efficacy of such programmes. The present study evaluates the efficacy of such a therapy dog programme in improving the well-being of university students. This research incorporates two components: (a) a pre/post within-subjects design, in which 246 participants completed a brief questionnaire immediately before and after a therapy dog session and (b) an experimental design with a delayed-treatment control group, in which all participants completed baseline measures and follow-up measures approximately 10 hr later. Only participants in the experimental condition experienced the therapy dog session in between the baseline and follow-up measures. Analyses of pre/post data revealed that the therapy dog sessions had strong immediate benefits, significantly reducing stress and increasing happiness and energy levels. In addition, participants in the experimental group reported a greater improvement in negative affect, perceived social support, and perceived stress compared with those in the delayed-treatment control group. Our results suggest that single, drop-in, therapy dog sessions have large and immediate effects on students' well-being, but also that the effects after several hours are small.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".