teestadas

The Social Lab project research journey started with studying addictions and the consumption of stimulants as a resolution for the pain experienced by someone. Johann Hari's work is a wonderful exploration of this. 

 

What I learned was that the cyclical cause of pain, in such cases, is psychological and primarily mental. I was researching the different accessible resources for anyone experiencing such kind of pain. Vaping, Nicotine Smoking, Tobacco smoking, and other substances-much of which is under-scrutiny and controversy for free access and consumption in many parts of the world.

​

I also found that those experiencing this nature of pain, are struggling to have their basic needs fulfilled. The most basic human need being that of conversations about their pain in a way that is perceived natural and acceptable by others around them. 

The basic human need to talk and have answers for some of the most obvious things around us- that human lifestyle is slowly changing. Friend circles or families are losing the patience or capacity to respond to simpler, random questions. So that's where mental health chatbots fulfill this fundamental need.

 

Woebot: being experimented in Stanford and Sim Simi: for teenage Cancer patients. Woebot uses natural language processing to respond to the user's statements and because the language processing may not be the most accurate, there have been issues.

​

Many of these apps only recently in 2018 got launched and are still getting tested and improved upon. BBC recently tested Woebot's answers and found it is still not accurately reporting self-harming behaviors. Biggest learnings are around what happens when conversations fail, it is however at a risk for the users, in some cases them being teenagers or younger. 

​

Some of the key features being tested and experimented are around:

1. Emotions and Negativity

2. Gratitude journalling

3. Stress and Language
   
    

​

Future thoughts/parking lot ideas: 

How might we use chatbots in solving and learning from field experiences of frontline health workers?

How might we use NLP data for rural healthcare?