Differentiating Workaholic Subtypes on Health and Wellness Outcomes
Bibliographic record
Abstract
The current study aimed to identify different subtypes of workaholics based on a combination of work engagement, motivation, perfectionism and job insecurity variables, and compare them on health and wellness outcomes. Perceptions of work-life balance, organizational culture and organizational climate were also examined to better understand the relationship between workaholic subtypes and their outcomes. A sample of n = 280 academics from universities in Ontario responded to an online self-report questionnaire. Cluster analysis showed the presence of three distinct workaholic subtypes that were named Engaged Workaholics, Perfectionist Workaholics and Job Insecure Workaholics. Univariate and multivariate analyses revealed significant differences between the clusters on health and wellness dimensions, whereby Engaged Workaholics reported significantly better outcomes compared to the other subtypes. Mediation analyses showed that lower levels of perceived work-life balance and higher levels of perceived work pressure culture explained poorer health and wellness outcomes, particularly for Job Insecure Workaholics. Moreover, it was shown that workaholic subtypes experienced different barriers to teaching and research, and attributed feelings of overwork to a variety of factors. This study is a first attempt to empirically distinguish workaholic subtypes based on personal and situational factors and provides evidence that different types of workaholics exist and outcomes are not the same for all. Findings of this work have important implications for employees and organizations, and could be used to inform policies and initiatives targeted at building healthy workplaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".