Happy but Exhausted: The Role of Passion in Explaining the Mitigated Psychological State of Health and Social Services Nonprofit Workers
Bibliographic record
Abstract
The goal of this study was to enhance our understanding of the psychological state of health and social services nonprofit workers using the concept of passion for work and the JD-R model. We proposed that both harmonious and obsessive passion influence the relationship between job resources and wellbeing at work, and that between job demands and burnout. To test this model, 774 workers completed an online survey. Results of path analysis indicate that job resources were positively associated with harmonious passion, while job demands negatively related to obsessive passion, although some cross-links appeared in both relationships. Results also showed that harmonious passion was positively related to wellbeing at work and negatively related to burnout, while obsessive passion was not related to burnout. Moreover, results showed that harmonious passion mediated the relationship between job demands and burnout, and that between job resources and wellbeing at work. However, obsessive passion was not a significant mediator of the job demands-burnout relationship. In addition, both types of passion were not significant moderators of the resources-wellbeing and demands-burnout relationships. Implications for research and practice regarding the psychological health and passion for work of nonprofit workers are discussed.
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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 | 0.006 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".