A longitudinal perspective on the associations between work engagement and workaholism
Bibliographic record
Abstract
The purpose of this two-wave longitudinal study was to examine the associations between work engagement and workaholism to better understand the psychological mechanisms underpinning high levels of work investment. These associations were examined in a sample of 514 employees using latent change models, allowing us to obtain a direct and explicit estimate of change occurring in both constructs over a 3-year period. These analyses relied on a bifactor representation of work engagement and workaholism, allowing us to properly disaggregate the global and specific levels of both constructs in the estimation of these longitudinal associations. To further enrich our theoretical understanding of the mechanisms at play in these relations, we also considered associations between these two constructs and employees’ levels of harmonious and obsessive work passion, two other facets of heavy work investment. Our results revealed the longitudinal independence of employees’ global levels work engagement and workaholism, showing that longitudinal associations between these two constructs occurred at the specific, rather than global, level. Harmonious work passion was only found to be associated to global and specific components of work engagement, whereas obsessive work passion was found to be associated with global and specific components of both work engagement and workaholism.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".