On the nature and covariates of workaholism profiles: A generalizability study across distinct types of employees
Bibliographic record
Abstract
Abstract We used a person‐centered approach to identify the profiles of workaholism observed in six samples, including 12 subsamples, of employees ( N = 7944). These profiles were defined while considering workers' global levels of workaholism, together with their specific levels of motivational, cognitive, emotional, and behavioral workaholism. We also considered the extent to which these profiles and their relations with covariates (work–family conflict, harmonious passion, and obsessive passion) generalized across all subsamples. Our results revealed a total of five profiles ( Preoccupied Employees with Very Low Global Levels of Workaholism, Disengaged Employees with Low Global Levels of Workaholism, Motivated and Involved Employees with Moderately Low Global Levels of Workaholism, Normative, and Preoccupied Employees with High Global Levels of Workaholism ). Systematically, the Normative profile was the most prevalent (43.7% to 63.2% across subsamples), whereas the Preoccupied Employees with Very Low Global Levels of Workaholism profile was the least prevalent (0.4% to 8.9%). The Preoccupied Employees with High Global Levels of Workaholism profile presented the highest levels of obsessive passion and work–family conflict, and the lowest levels of harmonious passion. Beyond their theoretical implications for research on workaholism, these results highlight how workaholism has highly similar implications across contexts.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".