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Composite visual representation of stress levels across age groups according to data from Statistics Canada.
Inspiration
COVID-19 has undoubtedly impacted the mental health of many, and there has been an increase in discourse over mental wellness since the beginning of the pandemic, as people experienced the isolation of quarantine. We wanted to further investigate the changes in mental health, specifically anxiety, across different age groups during the COVID-19 pandemic, and provide them with the help they need. Through our research using a dataset from Statistics Canada, we used Python and plotly to create graphs showing anxiety trends in the population, which gave us a rough projection of the stress levels in different age groups of Canadians, with the group 15-24 being the most stressed and a mostly linear downward trend as we moved up the age groups. We wanted to tackle this issue and bring support to those who need it most.
Our aim is to create a tool for young people to connect with government provided mental health professionals without any potential barriers. This interface also enables the government to collect more data surrounding this issue to improve their mental health resources. To reach people in our target audience, we decided that an app would be the most effective medium to connect young people with mental health guidance. However, as members of this age group, we understand that there could be situations where parents or guardians are unsupportive or dismissive about mental health issues and therefore these young people cannot access or afford any form of mental health support. This inspired us to create a solution -a discrete and accessible way for young people to receive mental health treatment, free of charge.
What it does
Our app acts as a tool for young people to report their mental health status, and receive personalized suggestions to government funded mental health professionals according to their responses. The app will act as a platform for private messaging and video calls with their chosen mental health professional without requiring a parent or guardian to sign off. Having a more accessible reporting tool can also reach a wider audience to collect more data that can be used to create a more realistic model of the issues that exist in our community and allow organizations, like the government to more accurately access the programs they currently have in place and make further improvements.
How we built it
To synthesize out data, we used a data class (Person) and made attributes for each of the categories we were measuring. We then opened and processed that data into the averages for each of the attributes for the given age groups, while filtering for any outliers. Then, we defined 12 empty lists: 6 lists for each age group, each of which will later consist of data class instances, and another 6 lists (1 per attribute) that will contain the average of the attribute for each age group and sorted each Person into the correct age group. Next, a nested for loop will go through each age group within each attribute and calculates the averages and adds it to a list. Another nested loop is used to a dictionary that maps the group number to a list of the averages for each attribute for that group. And then it calls another function to plot the data. The final function will take in a dictionary and a list of strings of attributes in order to plot the data. We obtain the first element of each of the averages list (this tuple contains the range for the attribute). Then we create a figure and add traces to make 8 graphs, one for each of the 6 attributes, one for the composite factor, and one showing all of the lines together. We also made a menu of buttons that when clicked, display each of the graphs.
After we made this graph, we used our findings to invent a mental health app aimed to help the statistically most stressed age group, particularly, teens who might not have the funds and resources to obtain mental health help. We used Figma to mock up our app, and show features like the evaluation quiz, graphs, private messaging and video call.
Challenges we ran into
One of the major challenges we faced was cleaning and unifying the data into a graph that accurately represented the issues we wished to address. Some limitations we faced in the data was that for each category ie. Anxiety score, there were some participants who did not answer and in the data, this translated to a number ie. 9 which shifted the overall average. Since this number was different for each of the categories, we had to go through each list and remove any data representing no answer to ensure an accurate average. Looking deeper into the data, we also found that the way some of the questions were structured, the scale was actually reversed ie. 1 is the best and 5 is the worst vs 1 being the worst and 5 being the best. To adapt this data to fit to match the other categories, we used the absolute value built-in function to reverse the direction ie. abs(1-6) to reverse 1 to 5 on a 1-5 scale.
Accomplishments that we're proud of
We're proud of making our project based off of real world data, to bring help to those who need it the most in our community.
What we learned
We've learned a lot about the process of first developing an idea by cleaning and visualizing data, and then creating a solution and bringing it to life through Figma. Making sure to focus on our main initiative and not deviate too far with additional features was definitely a challenge, but we learned to actively communicate, which made the job much easier.
What's next for Untitled
Moving forward, we would like to connect with more organizations geared towards providing mental health help to teens to increase the variety and amount of support we are able to provide.
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