Self‐Reported Utilization of Preventive Health Services by Retired Employees Age 65 and Older
Bibliographic record
Abstract
OBJECTIVES: Increased utilization of preventive services among the aging has been associated with improved health status and decreased medical costs. We sought to examine the use of the Health Risk Appraisal (HRA) in benchmarking compliance and characterizing those retired employees who met preventive service guidelines. DESIGN: A retrospective cohort study of retired employees age 65 and older. SETTING: Nation-wide health promotion program offered by General Motors Corporation. PARTICIPANTS: 59,670 retired General Motors employees age 65 and older who participated in a nationwide mailed HRA health promotion program. MEASUREMENTS: Preventive health services compliance was measured using selected HRA questions. Gender, HRA participation patterns, overall health risk status, medical plan selection and disease status were examined as predictors of increased compliance. Multivariate logistic regression models were developed to test the relative contributions of participant characteristics to increased utilization. RESULTS: The self-reported HRA data indicated that compliance levels were higher than national averages. The Healthy People 2000 goals for the preventive services studied were met and exceeded (with the exception of tetanus immunization). Higher compliance was associated with being male, younger than 70 years, multiple-year HRA participation, overall low risk status and HMO insurance plan selection. CONCLUSION: The results from the HRA indicated that this population participated at a higher level than a comparable national sample exceeding goals set by Healthy People 2000.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".