Examination of the Relationship Between Theory-Driven Policies and Allowed Lost-Time Back Claims in Workers' Compensation: A System Dynamics Model
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to develop a model that evaluates the impact of policy changes on the number of workers' compensation lost-time back claims in Ontario, Canada, over a 30-year timeframe. The model was used to test the hypothesis that a theory- and policy-driven model would be sufficient in reproducing historical claims data in a robust manner and that policy changes would have a major impact on modeled data. METHODS: The model was developed using system dynamics methods in the Vensim simulation program. The theoretical effects of policies for compensation benefit levels and experience rating fees were modeled. The model was built and validated using historical claims data from 1980 to 2009. Sensitivity analysis was used to evaluate the modeled data at extreme end points of variable input and timeframes. The degree of predictive value of the modeled data was measured by the coefficient of determination, root mean square error, and Theil's inequality coefficients. RESULTS: Correlation between modeled data and actual data was found to be meaningful (R(2) = 0.934), and the modeled data were stable at extreme end points. Among the effects explored, policy changes were found to be relatively minor drivers of back claims data, accounting for a 13% improvement in error. Simulation results suggested that unemployment, number of no-lost-time claims, number of injuries per worker, and recovery rate from back injuries outside of claims management to be sensitive drivers of back claims data. CONCLUSION: A robust systems-based model was developed and tested for use in future policy research in Ontario's workers' compensation. The study findings suggest that certain areas within and outside the workers' compensation system need to be considered when evaluating and changing policies around back claims.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".