Testing the Dualistic Model of Passion Using a Novel Quadripartite Approach: A Look at Physical and Psychological Well-Being
Bibliographic record
Abstract
Objective: Passion research has focused extensively on the unique effects of both harmonious passion and obsessive passion (Vallerand, 2015). We adopted a quadripartite approach (Gaudreau & Thompson, 2010) to test whether physical and psychological well-being are distinctly related to subtypes of passion with varying within-person passion combinations: pure harmonious passion, pure obsessive passion, mixed passion, and non-passion. Method: In four studies (total N = 3122), we tested if passion subtypes were differentially associated with self-reported general health (Study 1), health symptoms in video gamers (Study 2), global psychological well-being (Study 3), and academic burnout (Study 4) using latent moderated structural equation modeling. Results: Pure harmonious passion was generally associated with more positive levels of physical health and psychological well-being compared to pure obsessive passion, mixed passion, and non-passion. In contrast, outcomes were more negative for pure obsessive passion compared to both mixed passion and non-passion subtypes.Conclusions: This research underscores the theoretical and empirical usefulness of a quadripartite approach for the study of passion. Overall, the results demonstrate the benefits of having harmonious passion, even when obsessive passion is also high (i.e., mixed passion), and highlight the costs associated with a pure obsessive passion.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".