A longitudinal study comparing the effort - reward imbalance and demand - control models using objective measures of physician utilization
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to compare the predictive validity of the demand-control and effort-reward imbalance models using objective measures of physician utilization. METHODS: Self-reports for psychosocial work conditions were obtained in interviews with 1,028 workers using the demand-control and effort-reward imbalance models. Physician utilization outcomes were obtained through linkage to the British Columbia Linked Health Database. Outcomes were any visit to a physician for mental health reasons and 30 or more physician visits for any reason. The predictive validity of both models was compared in a longitudinal study using logistic regression. RESULTS: Neither job strain nor effort-reward imbalance predicted either outcome. However, low esteem reward and low status control increased the risk for 30 or more physician visits by, respectively, approximately 60% and 30%. CONCLUSIONS: In a sample of middle-aged blue-collar current and ex-sawmill workers in Western Canada, followed prospectively, after controlling for sociodemographic and workplace confounders, and reducing the potential for adverse health selection into high-stress jobs, low esteem reward and low status control were associated with a significantly greater risk for 30 or more physician visits for any reason.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".