New study findings from the University of Illinois confirm that an employee wellness program doesn’t improve health or healthcare costs.
Here’s what will happen next:
Wellness critics will argue that wellness programs must cease at once.
Wellness profiteers will, once again, falsely claim that the studied program was atypical and that the researchers failed to report on measures such as mental health, energy levels, quality of life, or job satisfaction.
Here’s what should happen next:
We should be prepared to accept, based on a growing body of evidence, that typical wellness programs don’t deliver on their promise.
We should collaborate with employees to figure out how we can effectively support their wellbeing.
Research should be leveraged to improve employee wellbeing strategies. Circling the wagons around the status quo or interpreting studies simply as a yay/nay on employee wellbeing are both unproductive.
Check out Margaret Moore’s post, 20 Years to a Vision with Wings, chronicling the path of Wellcoaches Corporation (and wellness coaching) leading up to the organization’s 20-year anniversary.
I’m honored that Margaret, Wellcoaches Founder and CEO, included me amongst the founders of the wellness coaching field, and especially honored to be associated with the other distinguished innovators she mentions:
Our inspiring fellow founders of the field we now call health & wellness coaching included Sean Slovenski, Bob Merberg, Michael Arloski, and Linda Bark.
Congratulations to Margaret, Wellcoaches, and the 12,000 coaches they’ve trained across the globe, who over 20 years have shaped the field that has benefited the wellbeing of so many!
Got this survey question from an employee wellness organization:
Are you worried about you or your employees contracting Coronavirus (COVID-19)?
1. Not worried
5. Very worried
There’s a fundamental flaw with how the question is constructed. Suppose you’re very worried about your employees getting COVID-19, but not worried about getting it yourself. How would you answer this survey item, which combines both questions into one? We should only ask one question… per question.
This also may serve as an example of how Likert scales can be poorly applied. Likert midpoints, when used, usually represent a neutral response (in this case, answer number 3 would be something like “Neither worried or unworried,” or a better option — since it may not be possible to be neither worried or unworried — may have been to include an even number of response choices with no midpoint).
Here, the survey providers essentially offer 4 levels of worry and 1 level of not-worried. They might be able to adjust for this in their analysis, but more typically survey providers generate a mean average score, which will be meaningless in this non-linear scale.
This reinforces what most of us have learned about survey design, and serves as a reminder to consider, when we read results of surveys on important topics (like public health or employer sentiment), how the data was collected.
A headline in the Washington Post (April 26, 2020), based on the researchers’ conclusions at the time, blared, “In New York’s largest hospital system, 88% of coronavirus patients on ventilators didn’t make it.” (As of April 25, the headline wasn’t corrected. By May 5, it had been changed to read “…many patients on ventilators didn’t make it.”)
The ventilator mortality rate excluded from the denominator patients still in the hospital.
After an immediate outcry from others in the medical community, the research paper was corrected in JAMA online:
“The sentence reporting mortality for patients receiving mechanical ventilation should read, ‘As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital.'”
This is one example — the cornovirus pandemic has provided many — of the importance of proper denominators.
Denominators often are not scrutinized closely enough by journalists, by businesses, or by wellness professionals. The gross oversight described here reminds us that, without any background in statistics or data science, denominators used in calculations — when, indeed, they are used — are one reasonable starting point (population size is another) when interpreting data.