Worksite wellness program implementation: a model of translational effectiveness

Diane L Elliot, David P Mackinnon, Linda Mabry, Yasemin Kisbu-Sakarya, Carol A Defrancesco, Stephany J Coxe, Kerry S Kuehl, Esther L Moe, Linn Goldberg, Kim C Favorite, Diane L Elliot, David P Mackinnon, Linda Mabry, Yasemin Kisbu-Sakarya, Carol A Defrancesco, Stephany J Coxe, Kerry S Kuehl, Esther L Moe, Linn Goldberg, Kim C Favorite

Abstract

Occupational health promotion programs with documented efficacy have not penetrated worksites. Establishing an implementation model would allow focusing on mediating aspects to enhance installation and use of evidence-based occupational wellness interventions. The purpose of the study was to implement an established wellness program in fire departments and define predictors of program exposure/dose to outcomes to define a cross-sectional model of translational effectiveness. The study is a prospective observational study among 12 NW fire departments. Data were collected before and following installation, and findings were used to conduct mediation analysis and develop a translational effectiveness model. Worker age was examined for its impact. Leadership, scheduling/competing demands, and tailoring were confirmed as model components, while organizational climate was not a factor. The established model fit data well (χ (2)(9) = 25.57, CFI = 0.99, RMSEA = 0.05, SRMR = 0.03). Older firefighters, nearing retirement, appeared to have influences that both enhanced and hindered participation. Findings can inform implementation of worksite wellness in fire departments, and the prioritized influences and translational model can be validated and manipulated in these and other settings to more efficiently move health promotion science to service.

Keywords: Firefighter; Mediation model; Occupational wellness; Translation.

Figures

Fig 1
Fig 1
Hypothesized mediation model of worksite wellness implementation
Fig 2
Fig 2
Diagram of mediator model
Fig 3
Fig 3
Full mediation model. All outcome variables were predicted by their corresponding pre-test score. Model fit the data well (, CFI = .99, RMSEA = .05, SRMR = .03). Unstandardized path estimates and standard errors are shown. Predictors were allowed to correlate. Outcomes were allowed to correlate. Paths that were statistically significant at are depicted in bold. **p < 0.01, *p < 0.05, †p = 0.055

Source: PubMed

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