Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach
Bibliographic record
Abstract
This study aimed to investigate the interplay between burnout and work engagement. More specifically, we examined the energy and identification continua theorized to underlie the relationship between burnout and work engagement by simultaneously evaluating the factorial structure of the Maslach Burnout Inventory–General Survey (MBI–GS) and the Utrecht Work Engagement Scale (UWES). Results from Exploratory Structural Equation Modeling (ESEM) offered little support for these continua, suggesting that burnout and work engagement are not diametrical counterparts. Moreover, ESEM significantly altered the relationships burnout and work engagement hold with job demands and resources (i.e., work overload, job autonomy, and recognition), as well as health-related (i.e., psychological distress) and motivational (i.e., turnover intention) outcomes. These findings shed new light on the health-impairment and motivational processes theorized by the JD-R model.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".