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Ethics and integrity should be measured primarily by the oath of the Greek physician Hippocrates. Primum Non Noncera — First, do no harm ~ Drucker
Image Courtesy, edenpictures (8928257201) on Flickr In the field of social change this is the most important thing to remember - Good intentions does not always convert into good outcomes. The Drucker quote above is a great reminder for all of us.
I always knew the concept but never had a word for it and then I found it in Anti-Fragile by Nassim Taleb through the fantastic blog Farnam Street. Taleb says:
In the case of tonsillectomies, the harm to the children undergoing unnecessary treatment is coupled with the trumpeted gain for some others. The name for such net loss, the (usually bitten or delayed) damage from treatment in excess of the benefits, is iatrogenics, literally, “caused by the healer,” iatros being a healer in Greek. […] Medicine has known about iatrogenics since at least the fourth century before our era-primum non nocere (“first do no harm”) is a first principle attributed to Hippocrates and integrated in the so called Hippocratic Oath taken by every medical doctor on his commencement day. … The very notion of iatrogenics is quite absent from the discourse outside medicine (which, to repeat, has been a rather slow learner.)
This is a powerful concept. Caused by the healer in our case is possible in many different ways.
This is a key concept that needs to be implemented by all the programs, services and enterprises that work in the space. It is not enough to focus on the right outcomes, it is important to decrease the possibility of the wrong outcomes from happening. What does a bad outcome look like?
Some of them may be due to unintended consequences and some are genuinely due to the program design.
One of the best way to solve it is by using a technique called “theory of change” or sometimes called “program logic”. There are a variety of ways to implement this but the one I use works this way. Every program is a theory incarnate. All the activities that are conducted in the program are towards a particular outcome. Each step in the program activity are based on a number of assumptions. These assumptions will determine whether the activity is the right one to do. And, most importantly, these assumptions can be tested and verified. If they fail, we need to change the activity and if they are validated, then we can go ahead. In time, the entire chain of activities are validated and the outcomes are reached. In theory it is quite simple but in practice, like everything, its not. However, there is great value in implementing this process. Technically, this is how you can describe it.
A theory of change takes a wide view of a desired change, carefully probing the assumptions behind each step in what may be a long and complex process. Articulating a theory of change often entails thinking through all the steps along a path toward a desired change, identifying the preconditions that will enable (and possibly inhibit) each step, listing the activities that will produce those conditions, and explaining why those activities are likely to work. It is often, but not always, presented as a flowchart.
A logic model takes a more narrowly practical look at the relationship between inputs and results. It is often presented as a table listing the steps from inputs or resources through the achievement of a desired program goal. Some grant makers use separate logic models to chart the implementation components of theory of change.
In our work at TACSI, we believe in making things easy to use and to create processes that enable individuals to use complex but important ideas to create better outcomes.
We have taken a visual process to theory of change by making it interactive, collaborative and explicit. Let’s look at it through the example of the famous health program An apple a day keeps the doctor away.
What you need:
We start with the problem at the left (the purple postit) and the goal is to create outcomes (orange on the right, both short term and long term). In between, the actions (blue) help determine the steps to be taken to achieve the outcomes. The assumptions (green) for each step are made explicit.
This process enables a team to clearly understand how their program runs and why it creates change? Both are important. In most of the teams this is possibly the first time they have detailed a process in this fashion. Even if the process is clear, the assumptions are never discussed. And this is the most useful part of the process.
By identifying the most critical assumptions, they can be tested and validated and in the process the entire process is improved. Most importantly, we can find out where it does not work and in the process creates more harm than good and thus avoiding _iatrogenics._
This is the most powerful and useful way of testing and prototyping out your program.
Assumptions are important for a variety of a reasons. One major reason is that some of the fundamental assumptions will drive the whole program. For example, lets look at this idea to increase fitness.
Increase fitness program logic
If the problem as shown above is that there is too little physical activity and it could be totally true. The outcomes expected are increased fitness and in the long term reduced health service usage. Both are good goals to have.
However, based on the assumptions behind the problem, whether the reduced physical activity is due to cost, being busy or spending time with loved ones the programs you would create will be entirely different. The initial assumptions you will make are fundamental to the engagement of people and the success of the program. Depending on the context, any one of the three assumptions behind the problem identified can be true and in some cases all three. However, unless the context is identified the solutions will be wrong.
The key is to remember the idea of first, do no harm and consciously use tools like theory of change to avoid creating more harm than good.
So, why would individuals do this?
Shane Parish lays out three possible biases that will make people do things even when the evidence says otherwise.
The first thing that goes through my mind is incentive caused bias. What is the incentive for action? Is there an agency gap where the outcome from person doing the intervention is disconnected from the outcome for the person experiencing it?
In my experience this is powerful stuff. If your job depends on it, if your beliefs depend it, if you are clouded because of the need to finish your work and reach targets and a myraid other reasons we will avoid it.
Another big reason I think this happens is a lack of clear feedback loops between action and outcome. It’s hard to know you’re causing harm if you can’t trace action to outcome.
This is more common in the case of social programs. Most are not funded to do evaluation, tools like theory of change are not common. Definitely they are not implemented like a designer to make the process easier.
Shorter and longer feedback loops are critical for success in these cases.
And the third major contributor, I’d say is our bias for action (especially what we consider positive action). This is also known as, to paraphrase Charlie Munger, do something syndrome. If you’re a policy advisor or politician, or heck, even a modern office worker, social norms make it hard for you to say “I don’t know.” You’re expected to have an answer for everything.
This is a biggy. We all have a bias for action. We are doing our work because we believe in the cause. That cause drives us and in the process we forget whether our actions create more harm than good because of the wrong belief that _any action _is better than no action.
What’s your theory of change?
