Workplace Health: Engaging Business Leaders to Combat Obesity
Bibliographic record
Abstract
Worksites are an important setting to promote healthy behaviors as 143 million adults are employed full-time and spend 8-10 hours per day at the workplace. Participation in health promotion programs have been shown to have a “dose response” relationship with health care costs, meaning health care costs decrease as employee involvement in health promotion activities in the workplace increase. Also from the employer perspective, it is important to note that obesity is a risk factor for many other chronic conditions, diabetes, heart disease, and cancer and is known to be related to increase injuries and health care costs. Motivating employees to participate in a number of wellness activities may provide benefits not only for obesity prevention but other desired outcomes such as: risk reduction, risk avoidance, reduced health costs, and improved productivity measures. Employers should be concerned as forecasts suggest that by 2030, 42% of the adult population will be obese. In fact, among employers, the costs of medical expenses and absenteeism increase as employees become more obese. The cost burden of obesity (BMI 30 or greater) ranges from $462-$2,027 among men and $1,372-$2,164 among women in comparison to normal-weight employees. However, halting this trend over the next few decades by maintaining (vs. increasing) current prevalence of obesity could potentially save billions in medical care expenditures related to obesity. Employers can be part of the solution by offering workplace wellness programs and facilitating opportunities for physical activity, access to healthier foods and beverages, and incentives for disease management and prevention to help prevent weight gain among their employees.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".