Health Care Opinion Research 1

The objective of this experiment is to investigate whether vignettes/stories about people suffering because of the Covid-19 pandemic can evoke (1) emotional reactions and (2) increase support for health polices to increase insurance coverage. Four stories will be investigated. Two emphasize the suffering of other people who do not have adequate health coverage. Two emphasize the risk to self if others people are not covered.

2020-08-03

2020-11-30

Empathic State
All participants will fill out the Emotional Response Scale (e.g., Batson, et al., 1988, 1989, 1991, 1997, 2007). This scale includes a six-item measure of Empathic State.

Distress/Negative State
The Emotional Response Scale also includes a 12-item measure of Distress and Negative State.

Health policy outcome
Ten items mostly drawn from Kaiser Family Foundation Health Tracking Polls [5, 6] that measure participant’s opinions about increasing health care coverage.

The Emotional Response Scale has been used in numerous experiments by Batson and colleagues to measure Empathic State and Distress-Negative State after providing participants with an emotionally evocative story. The scale is made up of 18 emotions (examples: sympathetic, compassionate, alarmed, sad). A participant self-rates (on a scale from 1 to 7) how much they experienced each emotion after reading the story.

The health policy outcome are eight items drawn directly from [5], one KFF item modified by us, and one item created by us. Examples:

a. “Do you favor or oppose having a national health plan, sometimes called Medicare-for-all, in which all Americans would get their insurance from a single government plan?”

b. “Please tell me if you have a positive or negative reaction to each term: National Health plan.”

c. “Generally speaking, do you favor or oppose the federal government doing more to help provide health insurance for more Americans?”

Response options are: strongly (favor/positive), somewhat (favor/positive), somewhat (oppose/negative), strongly (oppose/negative). The “don’t know” response forms the middle category.

Principle of Care State
These is an eight-item scale intended to measure a state of thinking about moral principles to help other people. It parallels Bekkers and Ottoni-Wilhelm’s (2016) dispositional measure, and was created by Verkaik, Bekkers, and Ottoni-Wilhelm (2015).

Principle of Care State
The conditions are not intended to increase thinking about moral principles to help other people, but the conditions may unintentionally do this. The PoC State scale is included to check this.

The experimental design is between-subjects, 5 x 1.

The participants will be Amazon Mechanical Turk workers invited to complete a “Human Intelligence Task” (HIT) called “Evaluating Messages”. The inclusion criteria are: (1) U.S. citizen, (2) 18 years or older, (3) done at least one previous HIT, and (4) have completed 95% or more of their previous HITs.

Approximately one-fifth of the participants will be randomly assigned to each of the five conditions: Control, Suffering of Others 1, 2 and Risk to Self 1, 2.

After reading the stories participants fill out the Emotional Response Scale, the Principle of Care state measurement, the health policy questions (the Attention-Check question is in the middle of the health policy questions), demographics, the Manipulation-Check question, and the political identity question.

Here are the four story conditions in more detail:

Suffering of Others 1
Lisa Fields is a young, energetic freelance writer and single mother of two. Until recently, she worked for a national bookstore chain to provide health insurance for her family. At the start of the COVID-19 pandemic, Lisa got a call from the regional bookstore manager that the chain would be closing permanently. A few days later, she received a letter that she was also losing her family’s health insurance coverage.

Lisa has heart damage from chronic low levels of iron. This has led to multiple hospital stays over the years. She can’t afford insurance, but without it, she can’t pay for the medications she needs to control her iron levels. This increases Lisa’s risk of complications from COVID-19. It also leaves her family in danger of running out of money if one of them gets sick.

Having health coverage and access to care (including vaccinations when available) will reduce the risk that people like Lisa will get infected with COVID-19.

[Photo of Lisa Fields]


Suffering of Others 2
Lisa Fields, a young, energetic freelance writer and single mother of two, recently died as a result of complications from COVID-19.

She went to the hospital the first time she felt symptoms of the virus. The hospital check-in staff told her she “wasn’t that sick,” and that she wasn’t a high risk—even though Lisa told them she had asthma. She suspected at the time that they didn’t take her seriously because she didn’t have insurance.

Two days later, Lisa had trouble breathing and called for an ambulance. The EMT told her she didn’t need to go to the hospital because she was just having a panic attack. The next day, still struggling to breathe, Lisa went to the ER again. She was admitted and immediately put on a ventilator.

The young woman stayed on ventilation for 30 days, unable to see her children, before being moved to a long-term care facility. She died there within hours.
Having health coverage and access to care (including vaccinations when available) will reduce the risk that people like Lisa will get infected with COVID-19.

[Photo of Lisa Fields]

Risk to Self 1
COVID-19 spreads easily when a contaminated droplet gets in your eyes, nose, or mouth. Like when you’re standing next to a checkout clerk or hair stylist and they accidentally cough in your direction. It’s in this context that businesses around the country are opening again.

New studies show that the virus can remain suspended in the air and travel on air currents. This means that the very air that you breathe can infect you. Recently, the CDC reported on a church meeting of 61 people that included a single infected person. Within days, 32 had COVID-19, three were hospitalized, and two died.

The continued spread of the virus can turn everyday activities into worrisome events. Just being around your checkout clerk or stylist may lead to you catching the virus.

Fewer people with health coverage and access to care (including vaccinations when available) means the number of infected people around you will be higher. This increases the risk that you will get infected, or re-infected, with COVID-19.

[Photo of sneeze]

Risk to Self 2
An otherwise healthy woman in her 20s who contracted COVID-19 recently died from complications after a double-lung transplant.

The woman’s case of COVID-19 landed her in the hospital. For six weeks she was in the intensive care unit on a ventilator. Her lungs could not function at all. She eventually recovered from coronavirus, but her lungs were permanently damaged. Her kidneys and liver also began to fail. One doctor said, “As a result of the COVID, the patient formed these cavities inside the lung. Those cavities became infected, and that bacteria caused sepsis.” A lung transplant was the young woman’s only chance of survival.

The transplant operation was successful, but the woman died two weeks later from multi-organ failure triggered by the operation.

Having COVID-19 can lead to painful complications, including death. Fewer people with health coverage and access to care (including vaccinations when available) means the number of infected people around you will be higher. This increases the risk that you will get infected, or re-infected, with COVID-19.

[Photo of damaged lung]

Control
COVID-19, or coronavirus, is an infectious disease that has spread around the world. The World Health Organization declared the spread of COVID-19 a pandemic on March 11, 2020.
[No photo]

The randomization is by computer.

Individual (at the level of the individual MTurk worker).

No

n.a.

N = 525 MTurk workers (approximately). N = 105 per condition (approximately) is sufficient to detect a change of one-third of a standard deviation in the health policy scale and the Empathic State and Distress-Negative State scales. See “Power calculation” below.

N = 105 (approximately) in each of the five conditions.

Based on previous measurements from an MTurk sample of the standard deviation of our (sES = 1.65), we are powered at 80% to detect a difference between conditions of 1/3 sES. We are also 80% powered to detect a 1/3 sDNS difference in Distress-Negative State. Based on a previous KFF Health Tracking Poll [5} we are powered at 80% to detect a difference between conditions of 1/3 of a standard deviation in the health policy outcome variable.