Crafting a Foundation for Evaluating a Worksite Wellness Program
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Abstract
Background: Businesses have been exposed to many positive accounts of the benefits of employee wellness to improve employee performance as well as reduce health and injury claims costs for the employer. However, many do not have the tools or experience to effectively demonstrate the benefits of a workplace wellness program for their own management and other stakeholders.
Purpose: This paper presents a) basic components for evaluating a workplace wellness program, b) observations gleaned from evaluating a wellness project, and c) a design for a simple evaluation system that provides information about the efficacy of the wellness program and establishes a foundation for more formal wellness program evaluation.
Setting: Maine
Intervention: Evaluating a wellness program and creating a system of measures for a sustainable evaluation system.
Research Design: Steps in wellness program evaluation are delineated (common definitions, evaluation design, i.e., logic model, data collection tools, data collection, comparison analyses, and ROI) and carried out. Particulars for a sustainable wellness program evaluation are rendered.
Data Collection and Analysis: We describe how to apply the process and measurements depicted in the logic model. We illustrate a method for calculating return on investment (determine the ratio of known wellness program costs to decreases in injury claims costs and sick leave costs). We recommend components for a sustainable evaluation system based on our experience in actualizing the logic model.
Findings: Applying the analyses we found positive benefits of employee wellness in our Maine DOT case. Workers’ compensation hours claims dropped from 875 hours in 2006, to 236 hours in 2007. Strains contributed roughly 17% of the overall injury costs reported for all three years, and almost one third of the costs for 2005. We computed a four-year ROI of $2.90. To help establish regular and routine wellness program evaluation we describe and recommend additional data sources and measurement points.
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