Workaholism profiles: Associations with determinants, correlates, and outcomes
Bibliographic record
Abstract
The present series of studies examines how the two dimensions of workaholism (working excessively and compulsively) combine within different profiles of workers. This research also documents the relations between these workaholism profiles and a series of correlates (psychological need thwarting) and adaptive and maladaptive work outcomes. In addition, this research investigates the role of emotional dissonance and employees' perceptions of their workplaces' psychosocial safety climate (Study 1, n = 465), as well as job demands, resources, and perfectionism (Study 2, n = 780) in the prediction of profile membership. Latent profile analysis revealed four identical workaholism profiles in both studies. In Study 1, emotional dissonance predicted a higher likelihood of membership in the Very High, Moderately High, and Moderately Low profiles relative to the Very Low profile. In contrast, Study 2 revealed a more diversified pattern of predictions. In both studies, levels of need thwarting were the highest in the Very High and Moderately High profiles, followed by the Moderately Low profile, and finally by the Very Low profile. Finally, in both studies, the most desirable outcomes levels (e.g., lower levels of work–family conflict and emotional exhaustion, and higher levels of perceived health) were associated with the Very Low profile, followed by the Moderately Low profile, then by the Moderately High profile, and finally by the Very High profile. Practitioner points The most desirable outcomes are associated with the profile characterized by the lowest levels of workaholism. Emotional dissonance predicts a lower likelihood of membership in the profile characterized by the lowest levels of workaholism. Levels of need thwarting are the lowest in the Very Low workaholism profile. High levels of socially prescribed perfectionism are associated with an increased likelihood of membership into the Very High workaholism profile. Reducing emotional dissonance, need thwarting, and socially prescribed perfectionism may help to reduce workaholism, in turn leading to more positive outcomes.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".