Passion, music, and psychological well-being
Bibliographic record
Abstract
Passionate music engagement is a defining feature of music fans worldwide. Although benefits to psychosocial well-being are often experienced by fans of music, some fans experience maladaptive outcomes from their music engagement. The Dualistic Model of Passion proposes that two types of passion—harmonious and obsessive—are associated with positive and negative outcomes of passionate engagement, respectively. This model has been employed in research on passion for a wide range of pursuits including music performers, but not for passionate listeners. The present study employed this model to investigate whether (1) harmonious passion for music is associated with positive music listening experiences and/or psychological well-being and (2) obsessive passion for music is associated with negative music listening experiences and/or psychological ill-being. Passionate fans ( n = 197) of 40 different musical genres were surveyed about their experiences when listening to their favorite music. Measures included the passion scale, affective experiences with music, and psychological well-being and ill-being. Results supported the Dualistic Model of Passion. Structural equation modeling revealed that harmonious passion for music predicted positive affective experiences which, in turn, predicted psychological well-being. Conversely, obsessive passion for music predicted negative affective experiences which, in turn, predicted psychological ill-being. The findings suggest that the nature of passionate engagement with music has an integral role in the psychological impact of music engagement and implications for the well-being of music fans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".