Moving beyond the Typologies of Managed Care: The Example of Health Plan Predictors of Screening Mammography
Bibliographic record
Abstract
OBJECTIVES: To develop a framework of factors to characterize health plans, to identify how plan characteristics were measured in a national survey, and to apply our findings to an analysis of the predictors of screening mammography. DATA SOURCE: The primary data were from the 1996 Medical Expenditure Panel Survey. STUDY DESIGN: Women ages 40+, with private insurance, and no history of breast cancer were included in the study (N = 2,909). We used multivariate logistic regression to estimate mammography utilization in the past two years relative to health plan and demographic factors. Health plan measures included whether there is a defined provider network, whether coverage is restricted to a network, use of gatekeepers, level of cost containment, copayment and deductible amounts, coinsurance rate, and breadth of benefit coverage. PRINCIPAL FINDINGS: We found no significant difference in reported mammography utilization using a dichotomous comparison of individuals enrolled in managed care versus indemnity plans. However, women in health plans with a defined provider network were more likely to report having received a mammogram in the past two years than those without networks (adjusted OR= 1.21, 95 percent CI = 1.07-1.36), and women in gatekeeper plans were more likely to report receiving mammography than those without gatekeepers (adjusted OR = 1.18, 95 percent CI = 1.03-1.36). Restricted out-of-network coverage, use of cost containment, enrollee cost sharing, and breadth of benefit coverage did not appear to affect mammography use. CONCLUSIONS: It is important to examine the effect of individual health plan components on the utilization of health care, rather than use the traditional broader categorizations of managed versus nonmanaged care or simple health plan typologies.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".