Quick Summary:
- What real-world problem is your organization facing?
- Map out your scenario on Mazetec, linking decisions to outcomes
- Send it out to your team so they can start making the right decisions and put it into practice.
- This case shows how you can change people’s behavior with a research article and Mazetec.
- Be sure to check out the gamified Scenario.
How long do you think it takes published research to be put into practice?
Often called the “bench to bedside” problem, the most common answer to this question is that it takes an average of 17 years for research evidence to impact clinical practice (Morris, 2011).
The research-to-practice gap requires “translation” of evidence-based findings into practical applications for clinical providers, customarily delivered through training in order to change how practitioners deliver care, e.g., apply a new intervention to a real-world problem.
The aim of the research is to find a new way to solve a problem, and then convince people to adopt the new and beneficial behavior until it becomes a habit, a process researchers have found takes about two months (Lally, 2010).
In the field of suicide prevention, delays in adopting evidence-based, newly-published research findings costs lives, just as substantial lag times in the development and testing of anti-viral vaccines result in higher infection rates and increased morbidity and mortality until the vaccine is distributed.
A Research to Practice Example Using Mazetec
In early 2020, the flagship journal of the American Psychological Association (The American Psychologist) published an article entitled, “Assortativity of suicide-related posting on social media” by Ian Cero and Tracy Witte. In brief, these researchers reported out a new method by which a school, university, military unit, employer, or any organization with identifiable members could employ a novel case identification screening strategy to locate persons in that organization who may be at elevated risk for suicidal behaviors.
Similar to “contract tracing” used by public health professionals to find individuals exposed to a virus, Witte and Cero developed a “relationship tracing” method based on their network analysis of the “propensity of similar people to be socially connected with one another more often than their dissimilar counterparts.” Using big data (64 million Twitter posts from 17 million users), they were able to establish that “assortative patterns can be exploited to improve the true-positive rate of suicide risk screenings.”
In other words, this unique research suggests a time-saving, faster, and more cost-effective way to find people at risk for suicide in a given population. Since early case detection is key to preventing suicide attempts and deaths, this valuable finding, like so many others, may rest somewhere on a shelf for what may be more than a decade before anyone puts this technique into practice.
Case Study
A Mazetec Scenario in 10 days, not 10 years
This article has immediate implications for policy and practice. The author created a real-world Mazetec practice scenario showing school health professionals how to use this new knowledge to facilitate finding youths at elevated suicide risk in a school of 1,000 students.
Thousands of U.S. students die by suicides in public or private school every year. Such deaths have major social, emotional, and system impacts, and none goes unnoticed. Any death by suicide, inside a closed system (such as a high school or organization) may raise the risk of suicide contagion in those exposed.
But how do you find the exposed students who may now be at elevated risk?
This is a frequent real-world problem encountered by school health professionals (nurses, psychologists, social workers and counselors).
The challenge presented can be framed as follows:
The challenge: Quickly find any other youths in the school who may be at elevated risk for suicide and intervene as necessary.
The tool: Use any sort of screening tool or personal screening interview to locate youths experiencing suicidal ideation and provide resources.
The problem: You are on your own and only have time to screen 50 youths out of 1,000.
Using this real-world scenario, the author constructed a gamified challenge in which the user learns to use the new research to locate other children at risk. By collaborating directly with the research team to assure fidelity to their findings via peer review, the following research-to-practice training tool was produced, tested, and released to the public in 10 days, not 10 years.
Here is a quick start guide to build evidence-based learning scenarios:
- Find peer-reviewed, published and actionable research.
- Create and build a draft scenario-based learning to Mazetec (1 day).
- Conduct a quick beta with close colleagues (early peer review, 1 week).
- Use this first beta to refine and polish the scenario (2 hours).
- Using the Mazetec “collaborate function” invite the author(s) of the research to collaborate on editing for fidelity to findings and “goodness of fit” from research-to-practice. (An hour or two over 1 to 2 weeks).
- Publish and distribute the learning tool.
For an example of this process from published article to finished training product, here is a link to the abstract of the original Cero and Witte 2020 article:
https://psycnet.apa.org/record/2019-32621-001
And here is the link to the finished 2020 Mazetec training tool:
https://app.mazetec.org/player?uuid=9e6a9662-6e1f-4265-8b3e-6ac09880b635
An evaluation of the utility and benefits of this training are underway.
References:
Cero, I., & Witte, T. K. (2020). Assortativity of suicide-related posting on social media. American Psychologist, 75(3), 365–379.
https://doi.org/10.1037/amp0000477
Lally, P., Jaarsveld, M., Potts, H., & Wardel, J. (2010). How are habits formed: Modeling habit formation in the real world. European Journal of Social Psychology, 40, 9980-1009. DOI: 10.1002/ejsp.674
Morris, Z.S., (2011). The answer is 17 years, what is the question: understanding time lags in translational research. Journal of the Royal Society of Medicine.
