Cognitive abilities and probabilities of adherence to containment measures against the COVID-19 pandemic

The emergence of COVID-19 has created the need for new policies based on social distancing and hygiene measures. An early study¹ found that working memory (WM) capacity could predict individual differences in individuals’ social distancing, although other psychological factors (e.g., depressed mood, anxious feelings, consciousness) and socioeconomic indicators were not relevant contributors in the investigation. The authors advocated that WM capacity was a good predictor of cognitive abilities, directly impacting the process of decision-making involved in adherence to containment measures.

Cognitive abilities can also be predicted by an individual’s educational level, in congruence with the WM of an individual. Educational attainment is typically employed as a surrogate measure cognitive ability, and earlier studies have shown its association with a higher level of intelligence.¹ Other research² has shown that a range of demographic factors, such as income, age, educational level, and personality traits from the Five-Factor Model, could be linked to coping strategies regarding COVID-19 protective measures, and that the higher one’s level of Conscientiousness, the more one is likely to adhere to protective measures.³

Due to the non-convergence of these findings, we propose investigating the impact of cognitive abilities on adherence to social distancing and hygiene measures prescribed to mitigate the impact COVID-19 and how personality traits could be used to understand this impact further. Previous studies used statistical techniques based on linear models.¹-³ Therefore, direct relations were investigated, but latent probabilities of adherence were not.

Our study performed a latent profile analysis (LPA) with educational level as a covariate to pursue patterns of explanation of the heterogeneity in individual response to COVID-19 containment measures. A questionnaire was designed, with items divided into two domains: self-care during the pandemic and importance of containment measures, including items focusing on oneself and other people. Because educational level presented an impact on the profile, we then used latent transition analysis (LTA) to determine whether respondents’ subgroups based on education level could be implicated in different probabilities of endorsing a given behavior related to protective measures.⁴,⁵

A two-profile model suggested a better explanation of the transition probabilities in respondents’ heterogeneity. Table 1 presents the item-response probabilities.

Table 1
Transition probabilities in respondent heterogeneity, controlled by educational level

	Response categories
	DT		D		C		CT
Item	Low	High	Low	High	Low	High	Low	High
I am more concerned with getting COVID-19 than concerned with the possibility of others getting it.	0.46	0.42	0.41	0.54	0.01	0.00	0.12	0.04
I’m more concerned with the people around me getting COVID-19 than myself.*	0.08	0.08	0.39	0.63	0.02	0.02	0.51	0.27
Much of what is said about COVID-19 is fake.	0.69	0.61	0.23	0.37	0.00	0.00	0.09	0.02
People are exaggerating the measures taken against COVID-19.	0.69	0.62	0.25	0.35	0.01	0.00	0.05	0.03
I didn’t change my routine because of COVID-19, since where I live, it doesn’t exist.*	0.02	0.06	0.18	0.47	0.01	0.03	0.79	0.44
I have a hard time following the recommendations against COVID-19 because they harm me at work.*	0.57	0.63	0.22	0.30	0.01	0.00	0.20	0.06
Following the recommendations against COVID-19 is a problem, as I need to help one or more people outside my home.*	0.03	0.05	0.12	0.19	0.10	0.50	0.74	0.26
It is tough for me to follow the recommendations against COVID-19, as I have a disease, and I need to treat myself outside my home.*	0.00	0.04	0.02	0.16	0.05	0.41	0.94	0.39
I’ve been losing (or making less) money since the COVID-19 pandemic.*	0.00	0.03	0.02	0.17	0.13	0.59	0.84	0.22
I have suffered a lot from not meeting with my family and friends because of the COVID-19 pandemic.*	0.01	0.11	0.13	0.23	0.10	0.45	0.76	0.22
I am very concerned about people in my family or friends who are at risk for COVID-19.*	0.01	0.03	0.00	0.13	0.05	0.34	0.94	0.50
I am very concerned because I am a risk group for COVID-19.	0.61	0.78	0.14	0.13	0.09	0.06	0.15	0.03

• C = agree; CT = agree completely; D = disagree; DT = disagree completely; High = profile with a higher level of consciousness; Low = profile with a lower level of consciousness).
• Bold type denotes significant differences among transition probabilities.
• ^*
  Items impacted by educational level.

By taking a closer look at the items, the profile class division seems to occur according to the level of Conscientiousness trait observed in each group, which would be in line with previous studies.¹-³ Under those circumstances, a two-profile model is better explained according to the level of Conscientiousness when education is a covariate. Based on these findings, government and health leaders should take the educational level of population subgroups into account when proposing containment measures, considering not only which actions should be proposed but how they should be proposed so they could be more efficient. Finally, future studies should consider investigating the associations among COVID-19 protective measures and direct measures of cognitive abilities and personality traits (both normal and pathological) in a multilevel perspective.

References

• ¹
  Xie W, Campbell S, Zhang W. Working memory capacity predicts individual differences in social-distancing compliance during the COVID-19 pandemic in the United States. Proc Natl Acad Sci U S A. 2020;117:17667-74.
• ²
  Volk AA, Brazil KJ, Franklin-Luther P, Dane AV, Vaillancourt T. The influence of demographics and personality on COVID-19 coping in young adults. Pers Individ Dif. 2021;168:110398.
• ³
  Carvalho LF, Pianowski G, Gonçalves AP. Personality differences and covid-19: are extroversion and conscientiousness personality traits associated with engagement with containment measures? Trends Psychiatry Psychother. 2020;42:179-84.
• ⁴
  Bray BC, Smith RA, Piper ME, Roberts LJ, Baker TB. Transitions in smokers’ social networks after quit attempts: a latent transition analysis. Nicotine Tob Res. 2016;18:2243-51.
• ⁵
  Collins LM, Lanza ST. Latent class and latent transition analysis: with applications in the social, behavioral and health sciences. New York: Wiley & Sons; 2010.

Publication Dates

• Publication in this collection
  05 Apr 2021
• Date of issue
  Jul-Aug 2021

History

• Received
  27 Jan 2021
• Accepted
  13 Feb 2021