A Person-Centered Perspective on the Combined Effects of Global and Specific Levels of Job Engagement
Bibliographic record
Abstract
This study examines how the different dimensions of job engagement combine within different profiles of workers ( n = 264). This research also documents the relations between the identified job engagement profiles, demographic characteristics (gender, age, education, working time, and organizational tenure), job characteristics (work autonomy, task variety, task significance, task identity, and feedback), attitudes (affective and normative commitment), and psychological health (emotional exhaustion and ill-being). Latent profile analysis revealed four profiles of employees defined based on their global and specific (physical, emotional, and cognitive) job engagement levels: Globally Disengaged, Globally Engaged, Globally but not Emotionally Engaged, and Moderately Engaged. Employees’ perceptions of task variety and feedback shared statistically significant relations with their likelihood of membership into all latent profiles. Profiles were finally showed to be meaningfully related to employees’ levels of affective commitment, normative commitment, emotional exhaustion, and ill-being.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".