Undergraduate students with musical training report less conflict in interpersonal relationships
Bibliographic record
Abstract
Recent research has shown that formal musical training has a wealth of benefits in terms of cognition, mental health, social skills, and even speech perception. Of these benefits, there is strong support for a relationship between formal musical training and an improved ability to recognize emotions in speech prosody. Given this connection, interpersonal relationships stand to benefit from improved communication efficacy, which includes an improved ability to recognize emotions in speech. Interpersonal relationships rely on successful expression and interpretation of emotions in speech. If formal musical training can improve the perception of emotions in speech, it should indirectly benefit interpersonal relationship quality. The current study collected data from 197 undergraduate students about their formal musical training and interpersonal relationship quality through an online survey. The results showed that formal musical training accounted for 8% of the difference in relationship conflict but did not benefit relationship support or depth. While musical expertise does not necessarily improve relationship quality overall, it may help reduce conflict in relationships. Further research is needed, with participants who have greater musical expertise, to clarify the relationship between formal musical training and relationship conflict.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".