The Role of Passion in Psychological and Cardiovascular Responses: Extending the Field of Passion and Positive Psychology in New Directions
Bibliographic record
Abstract
The present study fills a void in research on passion by examining for the first time the role of passion in physiological responses. The aim of the study was to investigate the role of passion, and the mediating role of cognitive appraisals, in the psychological and physiological responses to a stressful situation related to one’s passion. Students (43 women, 12 men,Mage = 27.21 years), who were passionate for their studies, completed the Passion Scale for their studies and the Cognitive Appraisal Scale (assessing perceptions of challenge/threat). Then, they engaged in an education task under stressful conditions, and a subsequent unrelated leisure task under no-stress. Physiological reactivity was measured throughout the entire session and their perceptions of situational vitality and positive and negative emotions were assessed directly after the education task. Results showed that harmonious passion (HP) positively predicted challenge appraisals that, in turn, were positively related to positive emotions, vitality, and positive cardiovascular adaptation while engaging in the stressful education task, but less so with the leisure task (unrelated to one’s passion for academia). On the other hand, obsessive passion (OP) positively predicted threat appraisals. In turn, threat appraisals were positively related to negative emotions, negatively associated with vitality, and not related to cardiovascular reactivity. The present findings suggest that HP creates the onset of an adaptive psychological and physiological response whereas the response is less adaptive with OP.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".