Worksite Health Promotion: The Value of the Tune Up Your Heart Program
Bibliographic record
Abstract
Successful wellness initiatives at DaimlerChrysler Canada Incorporated (DCCI) led to a unique partnership between key stakeholders that allowed implementation of Tune Up Your Heart, a program aimed at improving workforce cardiovascular disease (CVD) risk. Volunteers were screened and stratified according to their CVD risk. Interventions were tailored to risk level and included goal setting, monitoring progress, and company-wide education programs. Outcome data (CVD risk and components of risk) were collected at study entry and after 18 months. The economic impact of the program was determined using a model based on subject movement across risk categories and historical claims data for life insurance, short- and long-term disability, prescription drugs, and casual absenteeism. Intervention participants (N = 343) demonstrated a significant (P = .0113) relative CVD risk reduction of 12.7%; 36% of participants lost weight, and average body mass index decreased from 28.4 to 28.2 (P = .0419). Average systolic and diastolic blood pressure significantly decreased (P < .0001 and P = .0221, respectively). Subjects reported increased adherence to recommended exercise and diet regimens, and the number of smokers decreased by 14%. The majority of subjects reported satisfaction with the program. Annual savings were estimated at Can$793 for the intervention group and Can$18,461 when projected to the entire workforce (N = 13,629). Savings were sensitive to cost weighting when subjects moved to a lower risk class but more robust to other parameters. The Tune Up Your Heart program significantly improved employee CVD risk profile, and was associated with savings for DCCI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".