Clarifying the inconsistently observed curvilinear relationship between workload and employee attitudes and mental well-being
Bibliographic record
Abstract
Despite converging theoretical arguments regarding non-linear relationships between workload and employee attitudes (i.e. job satisfaction) and mental well-being outcomes, prior empirical support for these curvilinear effects has been mixed. In this study we offer and test two potential explanations that may help to reconcile this discrepancy. First, existing workload scales do not assess the full range of workload, thereby making it difficult to detect curvilinear relationships. Second, outcomes typically examined are too distal and there are different mediators (i.e. boredom and frustration) that explain effects at the low and high ends of the workload continuum, respectively, which also serves to obscure curvilinear effects. We examined these possibilities in two North American samples (N = 499 and 493) that employed different designs (i.e. cross-sectional versus multi-wave surveys). Overall, we find support for our hypotheses; ability to detect curvilinear effects is enhanced when using too much/too little rating scales that capture the entire workload continuum. Furthermore, boredom mediated the impact of low workload on outcomes, whereas frustration mediated the impact of high workload on outcomes. Therefore, this study helps clarify why prior studies may have inconsistently observed non-linear relationships between workload and outcomes. We discuss the implications for both researchers and practitioners.
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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 | 0.009 | 0.027 |
| 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 | 0.001 | 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".