Nature, predictors, and outcomes of Nurses' trajectories of harmonious and obsessive passion
Bibliographic record
Abstract
Abstract This study sought to achieve a dynamic person‐centered understanding of the various harmonious and obsessive work passion trajectories observed among a sample of nurses, as well as the connections between these two types of work passion trajectories. Moreover, it sought to document the predictive role of workload, unfairness, harassment, and supervisor support in relation to these harmonious and obsessive passion trajectories, as well as the implications of these trajectories for a variety of outcomes related to attitude (i.e., turnover intention), psychological health (i.e., perceived psychological health and work fatigue), and behaviors (i.e., work performance, presenteeism, and absenteeism). A sample of 622 nurses was surveyed six times over a period of five months. Our results revealed that harmonious and obsessive passion trajectories matched five primary profiles, similar across the two types of work passion. Workload, unfairness, harassment, and supervisor support were associated with these trajectories in a way that mainly supported our expectations. Trajectories characterized by higher levels of harmonious passion and lower levels of obsessive passion were associated with higher levels of perceived psychological health and work performance, and with lower levels of work fatigue, turnover intention, presenteeism, and absenteeism. Conversely, trajectories characterized by lower levels of harmonious passion and higher levels of obsessive passion were associated with the most negative outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".