On the Role of Passion for Work in Burnout: A Process Model
Bibliographic record
Abstract
The purpose of the present research was to test a model on the role of passion for work in professional burnout. This model posits that obsessive passion produces conflict between work and other life activities because the person cannot let go of the work activity. Conversely, harmonious passion is expected to prevent conflict while positively contributing to work satisfaction. Finally, conflict is expected to contribute to burnout, whereas work satisfaction should prevent its occurrence. This model was tested in 2 studies with nurses in 2 cultures. Using a cross-sectional design, Study 1 (n=97) provided support for the model with nurses from France. In Study 2 (n=258), a prospective design was used to further test the model with nurses from the Province of Quebec over a 6-month period. Results provided support for the model. Specifically, harmonious passion predicted an increase in work satisfaction and a decrease in conflict. Conversely, obsessive passion predicted an increase of conflict. In turn, work satisfaction and conflict predicted decreases and increases in burnout changes that took place over time. The results have important implications for theory and research on passion as well as burnout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".