The effect of Employee Assistance Programs use on healthcare utilization.
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of Employee Assistance Program (EAP) use on healthcare utilization as measured by health claims. DATA SOURCES: A unique data set that combines individual-level information on EAP utilization, demographic information, and health insurance claims from 1991 to 1995 for all employees of a large midwestern employer. STUDY DESIGN: Using "fixed-effect" econometric models that control for unobserved differences between individuals' propensities to use healthcare resources and the EAP, we perform our analyses in two steps. First, for those employees who visited the EAP, we test whether post-EAP claims differ from pre-EAP claims. Second, we combine claims data of individuals who went to an EAP with those of individuals who did not use an EAP to test whether differences in utilization exist between EAP users and nonusers. DATA COLLECTION METHODS: From the EAP we obtained the date of first EAP contact for all employees who used the service, and from the company's human resources department we obtained limited demographic data on all employees. We obtained healthcare utilization claims data on all employees and their dependents from the company's two healthcare plans: a fee-for-service (FFS) plan and a health maintenance organization (HMO) plan. PRINCIPAL FINDINGS: We found that going to an EAP substantially increases both the probability of an alcohol, drug abuse, or mental health (ADM) claim and the number of ADM claims in the same quarter as EAP contact. The increased probability of an ADM claim persists for approximately 11 quarters after the initial contact, while the increased ADM charges persist for approximately six quarters after the initial EAP contact. CONCLUSIONS: Our results strongly suggest that the EAP is able to identify behavioral and other health problems that may affect workplace performance and prompt EAP users to access ADM and other healthcare. Consistent with the stated goals of many EAPs, including the one examined in this study, this process should improve individuals' health, family functioning, and workplace performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".