COVID-19 vaccine refusal is driven by deliberate ignorance and cognitive distortions

[CharonY]

An interesting study looks at factors related to COVID-19 refusal. There has been an ongoing debate whether hesitancy was fueled by lack of good information or whether there are other drivers.

This study focuses on how folks process information and found an important impact in the form of deliberate ignorance:

Quote

Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies.

https://doi.org/10.1038/s41541-024-00951-8

[studiot]

Thanks for posting this.

I see only people who are good with and have a computer were sampled.

Doesn't that skew the population ?

Quote

with the process-tracing methodology Mouselab³⁹ (see “Methods: Mouselab task”). In Mouselab, the attributes of objects—here, pieces of information on vaccine evidence—are hidden behind labeled boxes, and each attribute can be inspected, one at a time, by hovering the mouse cursor over the respective box (Fig. 2a).

 

 

[CharonY]

Not sure if being good with a computer is a criterion, but it should be noted that the goal of this study is more learning about processes and mechanisms involved related to vaccination hesitancy, rather than identifying population-wide parameters. 

The cohort is skewed to some degree as they used a commercial system which seeks out participants according to desired parameters and at minimum participants would need to sign on the platform, which can create a bias on its own. I wouldn't necessarily hold it against this study, as a) it is largely impossible to create perfectly representative cohorts (i.e. there is always some kind of bias, one just need to be aware if and how it impacts the outcome) and b) the main goal is behind the curtains, so to speak and not necessarily be representative of the US population (and they refer to studies looking at demographics for this information, instead).

Aside from the computer aspects the cohort is not very representative, with 60% female, and, if I read the graph correctly, having a higher educational level than average, but also having a lower income profile. I am just guessing, but I suspect that it might be linked to a motivation to register with the commercial platform . The authors noted some other limitations, including being paid a flat fee, but argue for that some issues are mitigated. 

The demographic limitation is also reflected in the study due to the lack of an analysis of their outcome related to it. Rather they just compared the predictive power of their findings relative to (previously established) demographic variables. So again, a focus more on mechanism, which is also focussed on in the discussion section. The study falls under the "motivated reasoning" type of research and I do agree that a larger and more representative cohort could be interesting to figure out finer patterns.