Can obsessive passion facilitate responses to job characteristics and help prevent psychological distress and turnover intention? A moderated mediation model
Bibliographic record
Abstract
Purpose Although the benefits of work passion are well documented, the role of obsessive work passion (OWP) in coping and functioning at work remains unclear. To enhance this understanding, we propose that OWP can facilitate employee responses to job characteristics (job demands and resources) and prevent psychological distress and turnover intention by reducing the frustration of basic psychological needs (autonomy, competence and relatedness). Design/methodology/approach Data were collected at two time points over a 12-month period from nurses working in the public healthcare sector (Québec, Canada). Findings Results support the main hypothesis and highlight the distinct contributions of psychological needs to these relationships. The indirect effects of frustrated needs for competence and autonomy appear particularly salient. Practical implications Although OWP is typically considered as an undesirable motivational force that impairs functioning and well-being, our results show that OWP can help individuals adapt to job demands and resources and reduce the associated individual (psychological distress) and organizational (turnover) costs. Originality/value By providing novel insights into the adaptability of employees driven by obsessive passion, our study has theoretical implications for the dualistic model of passion, the job demands-resources model and self-determination theory, as well as managerial implications for organizations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".