A comparison between the effort-reward imbalance and demand control models
Bibliographic record
Abstract
BACKGROUND: To compare the predictive validity of the demand/control and reward/imbalance models, alone and in combination with each other, for self-reported health status and the self-reported presence of any chronic disease condition. METHODS: Self-reports for psychosocial work conditions were obtained in a sample of sawmill workers using the demand/control and effort/reward imbalance models. The relative predictive validity of task-level control was compared with effort/reward imbalance. As well, the predictive validity of a model developed by combining task-level control with effort/reward imbalance was determined. Logistic regression was utilized for all models. RESULTS: The demand/control and effort/reward imbalance models independently predicted poor self-reported health status. The effort-reward imbalance model predicted the presence of a chronic disease while the demand/control model did not. A model combining effort-reward imbalance and task-level control was a better predictor of self-reported health status and any chronic condition than either model alone. Effort reward imbalance modeled with intrinsic effort had marginally better predictive validity than when modeled with extrinsic effort only. CONCLUSIONS: Future work should explore the combined effects of these two models of psychosocial stress at work on health more thoroughly.
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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 | 0.042 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".