Creation of University Wellness Program Healthy Eating and Active Lifestyle Supports: A Knowledge-to-Action Process
Bibliographic record
Abstract
With the burdens that preventable health conditions place on individuals, workplaces, and society, workplace wellness programs (WWP) are critical to ensuring employees have access to health promotion supports tailored to their work environments. Such programs are best guided by a knowledge-to-action (KTA) framework; a theoretically grounded, systematic process that considers the ongoing exchange of knowledge with employees to engage them in health behaviour change and to garner employers' support for the interventions. Therefore the purpose of this project was to develop, implement, and evaluate WWP healthy eating and active lifestyle supports at a university. A KTA process guided the consultations with employees and stakeholders that led to the development and implementation of a range of resource effective supports and the incorporation of wellness in the organization culture. A key support was the Wellness Passport that encouraged participation in scheduled WWP activities, as well as allowing for self-identified ones. Quality assurance assessments demonstrated a desire for a continuation of these WWP supports and activities. Dietitians, as health promotion leaders, can play key roles in the emerging field of WWPs. University dietetic and internship programs should consider adding WWP and KTA training components.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".