Work Motivations, Satisfactions, and Health Among Managers
Bibliographic record
Abstract
Individuals in managerial and professional jobs are now working longer hours for a variety of reasons. Building on previous research on workaholism and on types of passion, the results of an exploratory study of correlates of work-based passion and addiction are presented. Data were collected from 530 Canadian managers and professionals, MBA graduates of a single university, using anonymously completed questionnaires. The following results were noted. First, scores on passion and addiction were significantly and positively correlated. Second, managers scoring higher on passion and on addiction were both more heavily invested in their work. Third, managers scoring higher on passion also indicated less obsessive job behaviors, greater work and extrawork satisfactions, and higher levels of psychological well-being. Fourth, managers scoring higher on addiction indicated more obsessive job behaviors, lower work and extrawork satisfactions, and lower levels of psychological well-being. Fifth, managers scoring higher on addiction saw their world in dog-eat-dog terms and their organizational cultures as less supportive of work—personal life balance; this pattern was in the opposite direction among managers scoring higher on passion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".