Passion for a Cause: How It Affects Health and Subjective Well‐Being
Bibliographic record
Abstract
Using the dualistic model of passion (Vallerand et al., 2003), this research investigated how harmonious passion (HP) or obsessive passion (OP) for a cause can affect volunteers' health and subjective well-being. Three studies with volunteers for local (local emergency crises and community help) and international (humanitarian missions) causes assessed physical and psychological health using cross-sectional and longitudinal designs. Study 1 (N = 108) showed that HP was positively related to satisfaction with one's involvement in the cause and unrelated to physical injuries due to cause involvement. OP was unrelated to satisfaction but positively associated with injuries. Findings were replicated in Study 2 (N = 83). Moreover, self-neglect mediated the positive and negative effects of HP and OP, respectively, on injuries. Study 3 (N = 77) revealed that HP predicted an increase in satisfaction and health over a 3-month mission. OP predicted an increase in physical symptoms and a decrease in health. Furthermore, OP before a mission was positively related to self-neglect that was positively associated with physical symptoms after a mission. OP also positively predicted rumination that was conducive to posttraumatic stress disorder. HP was unrelated to these variables. Findings underscore the role of passion for a cause in predicting intrapersonal outcomes of volunteers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".