The Effects of Provider Choice Policies on Workers’ Compensation Costs
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of provider choice policies on workers' compensation medical and indemnity costs. DATA SOURCES/STUDY SETTING: Pooled cross-sectional analysis of administrative claims records for workers with work-related injuries primarily in 2007-2010 across 25 states (n = 4,489,729). STUDY DESIGN: We used linear and quantile regression analyses to evaluate differences in claim costs (medical and indemnity) based on whether policies give employers or injured workers control over the choice of provider. PRINCIPAL FINDINGS: We find no difference in average medical costs by provider choice policies, although a distributional analysis indicates higher developed medical costs for the costliest back injury cases in states where workers control provider choice. The evidence for indemnity costs is similar, although the point estimates also indicate (statistically insignificantly) higher average costs when policies give workers more control of the choice of provider. CONCLUSIONS: Our nuanced evidence suggests that policymakers seeking to reduce workers' compensation costs may need to focus on the highest cost cases in states where policy gives workers more control over the choice of provider, rather than the simpler and broader issue of whether policy gives workers or employers more control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".