Demands, control, and support: A meta-analytic review of work characteristics interrelationships.
Bibliographic record
Abstract
The job demands-control-support model (DCS; Karasek, 1979) is an influential theory for understanding how work characteristics relate to employee well-being, health, and performance. However, previous research has largely neglected theory-building regarding the interrelationships between job demands, control, and support. We remedy such theoretical underdevelopment by reviewing and integrating theory on the relationships between demands, control, and support to develop five hypotheses. We test our hypotheses within a meta-analytic framework using a set of 106 studies. Our results show negative demands-supervisor support and demands-coworker support relationships, but no significant demand-control relationship. Our findings also indicate positive control-supervisor support and control-coworker support relationships. Using the meta-analytic effect sizes, we also estimate two competing structural equation models intended to discern which theoretical model using DCS work characteristics to predict occupational strain and well-being is more consistent with our data. Our results suggest that job control and both sources of social support should be treated independently, as opposed to indicators of a shared latent factor, in terms of their prediction of well-being and job demands. Our study offers support for the usefulness of the DCS and more modern conceptualizations of the working environment in understanding the employee work experience and for predicting important work 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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".