We wanted to find out if patients recognized the context of the bucket list. We had several questions:
- How common was it?
- What exactly did people list on their bucket list?
- Did age and gender influence what people write on their bucket list?
To get the perspectives of Americans across the nation, we did an online survey.
Here is the simple question we asked:
Do you have a bucket list? If yes, please list the items on your bucket list in the order of importance.
Participants could enter up to five bucket list items in text boxes provided.
Three thousand and fifty six people participated in the project. Overall, highly spiritual people were most likely to have a bucket list .
We analyzed their responses and found six common themes:
#1. Desire to travel (78.5% of participants)
#2. Desire to accomplish a personal goal (78.3%)
#3. Desire to achieve specific life milestones (51%)
#4. Desire to spend quality time with friends and family (16.7%)
#5. Desire to achieve financial stability (16.1%)
#6. Desire to do a daring activity (15%)
People most likely to list travel were college-educated women (84.3%). Unmarried men 65 years and over were least likely to list travel (52.3%) on their bucket list. Women younger then 33 years of age (69.3%) were most likely and unmarried persons over 59 years of age (22.3%) were least likely to list achieving a specific life milestone. Participants 63 years and over were most likely to desire quality time with their family and friends.While 16% of participants from all participants listed the desire to become financially stable, African Americans were more likely to list this compared to all other racial and ethnic groups. Persons younger than 26 years of age were most likely to list a risky daring activity on their bucket list.
What research are you doing next?
We have a new and very large bucket list dataset. As it is a huge dataset, we cannot analyze it manually. Instead, we have to use machine learning techniques to analyze it. We are currently cleaning and formatting the dataset so that it is in a format that is amenable to machine learning. Next, we need to secure funding to support the programmers to analyze the data.
