An investigation into the validity of two measures of work engagement
Bibliographic record
Abstract
This study investigated the validity of two measures of work engagement (the Utrecht Work Engagement Scale (UWES) and the May, Gilson and Harter scale) that have emerged in the academic literature. Data were collected using surveys with 139 employees in the Auckland-based call centers of two finance organizations, to assess the validity of the two measures. Some evidence for convergent, discriminant and predictive validity was found for both scales, although neither showed discriminant validity with regard to job satisfaction. Overall, the three factors of the UWES (vigor, dedication and absorption) performed slightly better across analyses than the three factors from the May, Gilson and Harter (2004 May, D.R., Gilson, R.L. and Harter, L.M. 2004. The Psychological Conditions of Meaningfulness, Safety and Availability and the Engagement of the Human Spirit at Work. Journal of Occupational and Organizational Psychology, 77: 11–37. [Crossref], [Web of Science ®] , [Google Scholar]) measure (cognitive, emotional and physical). There are some important differences between the two scales, raising questions about how we should be measuring work engagement. The current use of different descriptions and measures means that findings will be specific to each of these. This limits generalizability across studies, which will both slow theoretical progress and reduce the ability of science to contribute to practice.
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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 | 0.029 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".