Evaluation of Get Healthy at Work, a state-wide workplace health promotion program in Australia
Bibliographic record
Abstract
BACKGROUND: Workplace health programs (WHPs) may improve adult health but very little evidence exists on multi-level WHPs implemented at-scale and so the relationship between program implementation factors and outcomes of WHPs are poorly understood. This study evaluated Get Healthy at Work (GHaW), a state-wide government-funded WHP in Australia. METHODS: A mixed-method design included a longitudinal quasi-experimental survey of businesses registered with GHaW and a comparison group of businesses surveyed over a 12-month period. Semi-structured interviews and focus groups with key contacts and employees of selected intervention group businesses and the service providers of the program were conducted to assess program adoption and adaptation. RESULTS: Positive business-level changes in workplace culture were observed over time among GHaW businesses compared with the control group. Multilevel regression modelling revealed perceptions that employees were generally healthy (p = 0.045 timeXgroup effect) and that the workplace promoted healthy behaviours (p = 0.004 timeXgroup effect) improved significantly while the control group reported no change in work culture perceptions. Changes in perceptions about work productivity were not observed; however only one third of businesses registered for the program had adopted GHaW during the evaluation period. Qualitative results revealed a number of factors contributing to program adoption: which depended on program delivery (e.g., logistics, technology and communication channels), design features of the program, and organisational factors (primarily business size and previous experience of WHPs). CONCLUSIONS: Evaluation of program factors is important to improve program delivery and uptake and to ensure greater scalability. GHaW has the potential to improve workplace health culture, which may lead to better health promoting work environments. These results imply that government can play a central role in enabling prioritisation and incentivising health promotion in the workplace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".