.png)
Most people have faced the frustrations of biased surveys and complex problems being oversimplified to the detriment of solving them. We have seen the drag model of Change Management that defines the targets, goals, and indicators we want to hit and often fails. Hindsight being 20/20, we fail to see that good people are working hard with tools that do not work with truly complex problems.
Research has found that when people work for explicit goals, it destroys intrinsic motivation. For example, when funding is tied to test scores teachers and administrators prioritize increasing those over preparing students for their future. And this is not a value statement on their actions, this is just an example of unintended consequences of well-meaning actions. Additionally, it is Goodhart's Law is well established within research communities as generalized by, "anthropologist Marilyn Strathern, “When a measure becomes a target, it ceases to be a good measure.”1 In its original form, Goodhart's law stated, “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” (Mattson et al., 2021)
So what do we do instead and how to we effectively drive positive change in Elder Care in Oregon?
In this effort we will be applying the principals and methods of Cynefin and Sense-making to leverage the community's dispersed knowledge, gather stories and data that allow us to identify signals and opportunities for change.
The process of gathering stories through Sensemaker removes much of the bias that is inherent in standard surveys by first asking the respondent to tell us a story about the topic and then to answer additional questions about the story that tell us what it means. By keeping the prompts open and allowing the respondent to tell us what it means we remove the survey writer's bias in writing and interpreting the survey. The meta data that is captured through the questions is immediately available via dashboards to begin looking for the emergence of novelty.
As data is gathered there will be additional opportunities for the community to review the stories, identify groupings of stories they want more of and define small, safe to fail experiments to try to increase them. The ongoing story collection and review allows the efforts to get real-time feedback on if the experiment was successful or harmful so that quick adjustments can be made until there is a clear enough path to make material investment.
Help us by providing stories, sharing the opportunity, and engaging in review and experimentation!