Promoting Staff and Physician Well-Being With a Single-Day Event: Event Satisfaction and Perceived Well-Being Benefits of an Addiction and Mental Health Sports Day
Bibliographic record
Abstract
Background: Addiction and mental health (AMH) professionals often experience high levels of burnout, which can lead to detrimental outcomes for patient care and safety and the AMH professional themselves. Interventions aimed at improving well-being can be useful to tackle the issues surrounding burnout. Specifically, implementing activity-based events (ABEs), which encourages individuals to be more physically active, can be useful in improving physical, mental, and social well-being. Alberta Health Services AMH, Edmonton Zone, implemented a Sports Day event to promote well-being as a way to help offset the risk of burnout. Methods: This was an online anonymous cross-sectional, postsurvey evaluation. The postsurvey was administered to 243 AMH staff and physicians who registered for Sports Day. Findings: The responses from 66 AMH staff and physician attendees indicated that individuals were highly satisfied with the event and felt that Sports Day promoted positive mental and physical well-being, helped to develop a sense of community, and strengthened colleague relationships. Conclusions/Application to Professional Practice: The results build on the literature examining the effects of single-day ABEs and can be implemented by health care organizations to promote staff and physician well-being via increased physical activity. Single-day ABEs, like Sports Day, can promote mental, physical, and social well-being. Organizing a sports day event is a feasible way to help offset the risk of burnout and is generally well received by individuals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".