ENHANCING EMPLOYEES’ HEALTH AND WELLBEING: DEVELOPING A SUCCESSFUL HOLISTIC WELLNESS CHALLENGE
Bibliographic record
Abstract
Improving the overall health and wellness of employees is an important focus of many organizations. Healthy employees experience more job satisfaction and productivity. The University of Calgary offers programs and services that promote healthy living, a healthy work environment, and respect the employees’ lives outside of work as well as their work life. Collaborating with the university’s WellBeing and Work Life department, the purpose of our community health promotion project was to develop and provide recommendations for the implementation of a successful holistic wellness challenge for faculty and staff. Drawing on the Population Health Promotion Model, the Community as Partner model, and the nursing process, a windshield survey, key informant interviews, a focused review of literature, and an environmental scan of 15 Canadian universities (U15) and Vanderbilt University (well-known for its excellent occupational health program). The information gained was used to create then pilot a holistic evidence-based challenge that aims to improve employees’ physical activity, exercise, mental health, social health, financial health, and nutrition. To increase community members’ awareness and participation in the wellness challenge, we developed brochures detail the health benefits to be gained and offer suggestions for implementing each component. Because our evidence-based recommendations are feasible and flexible with clear marketing strategies, they are more likely to be adopted by organizations and their employees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".