Bibliographic record
Abstract
Mental health problems are commonplace in university, with 85% of students reporting elevated stress and 32% reporting clinically significant problems (Eisenberg et al., 2007; Garlow et al., 2008). Thus, programs that allow for the mitigation of stress and negative mental health symptoms have important implications for student well-being. Several studies have demonstrated that Animal Visitation Programs (AVPs) have positive effects on university students’ mental health. However, when it comes to equivalency across studies, very few have comparable interaction times. Examining the relationship between interaction time and stress mitigation is important to balance treatment efficacy and efficiency. To test this, participants completed a mental arithmetic task to induce stress, and were then given 35-min to interact with a therapy dog, with measurements being taken during the interaction at times of 5-, 15-, 25-, and 35-min. Interaction time led to significant improvements in self-reported negative affect, stress, and anxiety, but these did not improve at the same rate. Specifically, these measures showed maximal improvement at times of 5-, 15-, and 25-min, respectively. These findings have two main implications. First, as stress, anxiety, and mood do not improve at the same rate, interaction times may vary depending on the intention of the interaction. Second, since no measures showed significant improvements after 25-min, these findings indicate that it would be beneficial for AVPs to limit interaction time to a maximum of 25-min, in order to maximize the efficiency of the these programs while most benefiting students’ mental health. Faculty Mentor: Eric Legge Department: Psychology (Honours)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".