Effects on Wellbeing of Exposure to Dog Videos Before a Stressor
Bibliographic record
Abstract
Animal-assisted intervention (AAI) has been used as a means of stress relief in clinical and general settings; however, animals are not always allowed in certain spaces. Adapting AAI to video or virtual mediums could improve accessibility and is temporally relevant given the recent shift to online interventions. The current study explored: (1) whether an active video (dog or nature) watched before a stressor would improve wellbeing more than tranquil videos; (2) whether exposure to a dog video improves wellbeing more than a nature video; and (3) whether exposure to either a dog or nature video improves outcomes more than exposure to a control video. One hundred and seven undergraduates were randomly assigned to watch one of five videos (active dog, tranquil dog, active nature, tranquil nature, and control) for 3 minutes and then complete a 3-minute stress task. Subjective (anxiety, stress, happiness, relaxation, positive affect, and negative affect) and physiological (blood pressure and heart rate) outcomes were collected at baseline, video, stressor, and recovery time points. Results showed that the activity level of the dog in the video did not influence outcomes. However, relative to the control group, the dog-video condition showed decreases in stress from baseline to video and a smaller decrease in stress from stressor to recovery. Additionally, relative to the nature-video condition, the dog-video condition showed a slightly higher increase in happiness scores from baseline to video. Lastly, relative to the control group, the nature-video condition showed increased relaxation scores from baseline to video and a larger decrease in relaxation scores from video to stressor. This research may inform the development of alternate modes of AAIs.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".