Bibliographic record
Abstract
Listening to music can induce relaxation, especially when participants select their own preferred music. However, the role of singing in relaxation is less clear. Some studies have examined group singing, or used singing to induce stress, but it remains unclear if singing in the absence of social stressors can decrease perceived stress. The current study examined this question. Participants (N = 392) rated their current mood before and after a stress inducing reflection task, then were randomly assigned to one of four music interventions: singing vs. listening to a self-selected vs. experimenter-selected song. Following this, participants completed another mood rating, along with questionnaires assessing musical sophistication, personality, demographics, and experience during the intervention. Results revealed a larger decrease in stress after singing compared to listening, although stress decreased significantly in both cases (both ps < .001). Our findings suggest that under certain conditions, singing may be slightly more effective than music listening for stress relief, and that the greatest decreases in stress occur for liked songs. This research is not only beneficial to students managing stress, but has implications for the wider population handling stress during the pandemic. An additional exploratory study examined arts-engagement during COVID-19 and its relationship to stress, anxiety, and coping strategies. Most participants (95.7%) engaged with the arts; relaxation being the most commonly reported feeling (82%). Increases in stress and anxiety correlated with the use of avoidant coping (both ps < .01), but not approach or arts coping. Future directions of these findings will be discussed. Department: Psychology Faculty Mentor: Dr. Kathleen Corrigall
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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| 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".