Exercise Preferences for a Workplace Wellness Program to Reduce Cardiovascular Risk and Increase Work Productivity
Bibliographic record
Abstract
OBJECTIVE: Workplace wellness programs can reduce cardiovascular risk and improve worker productivity; however, recruitment and adherence remain a challenge. Tailoring programs based on employee exercise preferences may address these concerns. METHODS: A total of 458 UCLA adult employees who responded to UCLA Bruin marketing e-mail completed a battery of questions regarding their exercise preferences (eg, preferred duration, intensity, type). Recruitment took place in June 2021. RESULTS: Participants prefer workplace wellness programs that (1) focus on improving multiple different aspects of physical health; (2) take place in a variety of locations; (3) were administered by a coach who is physically present; (4) occur 2-3 times per week for roughly 60 minutes each time; (5) include a range of intensities; and (6) consist of aerobic and weight training. CONCLUSIONS: Future studies should use these results to design future workplace wellness programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".