Project Overview

Problem

COVID-19 vaccine hesitancy has been recognized as a problem across nations. A resistance to getting vaccinated is emerging as a major hurdle, especially in the developing world, where vaccine access issues are still being gradually resolved. Persistent pools of unvaccinated people around the world could present a greater risk for the emergence of new variants of concern. Addressing people’s vaccine hesitancy is hence crucial to curb the spread of COVID-19, and to consequently avert hospitalizations and death.

Objectives

We intend to understand why people are hesitant about getting the COVID-19 vaccine. Hesitancy could not only occur within the unvaccinated population but also in a subset of people who already got vaccinated. Therefore, phase 1 of the project has the following objectives:

  1. Understand why people are hesitant to get the COVID-19 vaccines
  2. Understand ways to best elicit vaccine impediments from respondents
  3. Pinpoint what treatments will help people get vaccinated

Approach

We intend to use chatbot as a medium (on Facebook) to conduct conversations with people to understand how we can best achieve the above three objectives. We have run six pilots as of March 12, 2022, – 2 in the United States using Qualtrics on Lucid, and 4 in South Africa on Facebook. The eventual goal will be running this using multiple chatbots that enable the conversation to flow more naturally than in a survey format. We hypothesize that respondents are more likely to respond to our sensitive questions around vaccine hesitancy if the questions are asked more casually in an open stress-free setting. Therefore, in all version of the pilots, we make our tone as causal as possible (using emojis, GIFs, emphatic prompts) and include delays in the appearance of questions (and empathetic responses after each question) to make the conversation more authentic.

Current Analysis

Analysis script used is linked here. Relevant GitHub issue is linked here.

This analysis is based on 2127 respondents who completed the current pilot survey waves 4a, 4b, and 5.

We have split the detailed analyses into 3 sections:

  • Motivation impediments
  • Ability impediments
  • Treatments preferred
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Motivation impediments

Distributions

Associations

The correlation plots here aim to understand what demographic variables and ability impediments are directly correlated with each other. We want to filter out the demographic variables that are highly correlated with other demographic variables and the demographic variables that are not related to the motivation/ability impediment to minimize the number of questions respondents have to answer.

Since the correlation matrixes provided by ggcorrplot() shows the correlation coefficients between continuous variables, we mapped binary and ordinal variables to continuous variables.

Details on the mapping:

  • female: 1 if female, 0 if male
  • country: 1 if live in South Africa, 0 if not
  • income: 0 if the participant is unemployed, 1 if income < R5,000, 2 if income in R5,000 – R9,999, …, 6 if income > R100,000
  • education: 1 if the participant’s education < high school, 2 if education is high school, …, 6 if education is a graduate degree
  • religiosity: 1 if the participant is not very religious, 2 if somewhat religious, 3 if very religious
  • politics: 1 if the participant is conservative, 2 if moderate, 3 if liberal
  • location: 1 if the participant lives in rural, 2 if suburban, 3 if urban,
  • white: 1 if the participant is a white or caucasian, 0 if not
  • ability: 1 if the participant has the ability to get vax, 0 if not
  • motivation: 1 if the participant has the ability to get vax, 0 if not = treat_nchar: Character count of response for best treatment (free text) explanation

Blank correlation cells are not statistically significant.

Sentiment

Politeness

Receptiveness



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Ability impediments

Distributions

Associations

The correlation plots here aim to understand what demographic variables and ability impediments are directly correlated with each other. We want to filter out the demographic variables that are highly correlated with other demographic variables and the demographic variables that are not related to the motivation/ability impediment to minimize the number of questions respondents have to answer.

Since the correlation matrixes provided by ggcorrplot() shows the correlation coefficients between continuous variables, we mapped binary and ordinal variables to continuous variables.

Details on the mapping:

  • female: 1 if female, 0 if male
  • country: 1 if live in South Africa, 0 if not
  • income: 0 if the participant is unemployed, 1 if income < R5,000, 2 if income in R5,000 – R9,999, …, 6 if income > R100,000
  • education: 1 if the participant’s education < high school, 2 if education is high school, …, 6 if education is a graduate degree
  • religiosity: 1 if the participant is not very religious, 2 if somewhat religious, 3 if very religious
  • politics: 1 if the participant is conservative, 2 if moderate, 3 if liberal
  • location: 1 if the participant lives in rural, 2 if suburban, 3 if urban,
  • white: 1 if the participant is a white or caucasian, 0 if not
  • ability: 1 if the participant has the ability to get vax, 0 if not
  • motivation: 1 if the participant has the ability to get vax, 0 if not = treat_nchar: Character count of response for best treatment (free text) explanation

Blank correlation cells are not statistically significant.

Sentiment

Politeness

Receptiveness



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Treatments preferred

Distributions

Associations

The correlation plots here aim to understand what demographic variables and ability impediments are directly correlated with each other. We want to filter out the demographic variables that are highly correlated with other demographic variables and the demographic variables that are not related to the motivation/ability impediment to minimize the number of questions respondents have to answer.

Since the correlation matrixes provided by ggcorrplot() shows the correlation coefficients between continuous variables, we mapped binary and ordinal variables to continuous variables.

Details on the mapping:

  • female: 1 if female, 0 if male
  • country: 1 if live in South Africa, 0 if not
  • income: 0 if the participant is unemployed, 1 if income < R5,000, 2 if income in R5,000 – R9,999, …, 6 if income > R100,000
  • education: 1 if the participant’s education < high school, 2 if education is high school, …, 6 if education is a graduate degree
  • religiosity: 1 if the participant is not very religious, 2 if somewhat religious, 3 if very religious
  • politics: 1 if the participant is conservative, 2 if moderate, 3 if liberal
  • location: 1 if the participant lives in rural, 2 if suburban, 3 if urban,
  • white: 1 if the participant is a white or caucasian, 0 if not
  • ability: 1 if the participant has the ability to get vax, 0 if not
  • motivation: 1 if the participant has the ability to get vax, 0 if not = treat_nchar: Character count of response for best treatment (free text) explanation

Blank correlation cells are not statistically significant.

Sentiment

Politeness

Receptiveness