Impact of COVID-19-related knowledge on protective behaviors: The moderating role of primary sources of information.

This study assessed the modifying role of primary source of COVID-19 information in the association between knowledge and protective behaviors related to COVID-19 among adults living in the United States (US). Data was collected from 6,518 US adults through an online cross-sectional self-administered survey via social media platforms in April 2020. Linear regression was performed on COVID-19 knowledge and behavior scores, adjusted for sociodemographic factors. An interaction term between knowledge score and primary information source was included to observe effect modification by primary information source. Higher levels of knowledge were associated with increased self-reported engagement with protective behaviors against COVID-19. The primary information source significantly moderated the association between knowledge and behavior, and analyses of simple slopes revealed significant differences by primary information source. This study shows the important role of COVID-19 information sources in affecting people's engagement in recommended protective behaviors. Governments and health agencies should monitor the use of various information sources to effectively engage the public and translate knowledge into behavior change during an evolving public health crisis like COVID-19.

Introduction

The World Health Organization (WHO) declared COVID-19 a Public Health Emergency of International Concern (PHEIC) on January 30, 2020 [1]. The COVID-19 pandemic continues to cause significant global disruption across sectors ranging from healthcare to education to the economy. Millions of people around the world have been subjected to mitigation strategies, including stay-at-home restrictions, physical and social distancing, and mask wearing, all of which have necessitated substantial changes in individual behaviors for the collective good of the community. Despite recommendations by public health authorities, people have had varying levels of engagement with protective behaviors against COVID-19, and this has presented a major obstacle to the success of measures to control the pandemic [2-5].

Knowledge is shown to be positively associated with health protective behaviors [6-8]. During the current pandemic, an associated infodemic, defined as “an overabundance of information—some accurate and some not—”has made it challenging for people to find reliable and credible sources to acquire knowledge when they need it [9]. The start of COVID-19 as pneumonia of unknown aetiology [10] allowed for extensive speculation of the origins of the disease and the limited information at the time [11]. This led to delays in communication of the scientific knowledge about the disease by public health authorities. In addition, the rise of online communication mediums, such as social media, blogs, and podcasts, has resulted in user-generated content that is often disseminated without verification of its veracity and consumed at an unprecedented speed and scale by the public. A study conducted in six developed countries in April 2020 showed that while the majority of people used official news organizations as their primary source of information, about half of the participants reported also using Google or other online search and social media platforms for COVID-19-related information. Specifically, 25–53% of the participants across six countries reported using Facebook to obtain information on COVID-19 at least once over the past week, while 15–46% of the participants used YouTube for the same purpose [12].

The current challenge for public health authorities is, therefore, to strategize the dissemination of COVID-19-related information to counter the misinformation emanating from these easily accessible and often unregulated online sources and to deliver timely and correct information to the public supported by scientific evidence. During this pandemic, evidence has shown that people use a vast range of sources to get COVID-19-related information [13], and their choice of primary source reflects their trust in the legitimacy of these sources and affects their attitudes and vaccine uptake, as also supported by past research on vaccine hesitancy in general [14, 15]. Despite the growing volume of research on the COVID-19 infodemic, evidence is lacking on how the source from which people acquire COVID-19-related knowledge influences the association between knowledge and protective behaviors. Prior to COVID-19, the use of social media, including Wikipedia, blogs, and social networking services platforms, as a source of health-related information to affect behavior change was found effective but at the same time risky because of the trustworthiness of information depending on the information-seeking context and source [16-18]. Furthermore, no study to date has examined the role of different information sources in moderating the association between knowledge and behaviors while controlling for other factors.

In this study, we tested the hypothesis that different primary sources of COVID-19 information will act as an effect modifier of the relationship between levels of knowledge and self-reported engagement in protective behaviors. We focused on the primary source of information based on the idea that people’s choice of primary information source is a manifestation of their health consciousness and motivation for health-oriented behaviors [19]. The findings of this study have the potential to improve the targeting and effectiveness of risk communication strategies seeking to achieve behavior change.

Methods

Study participants and design

This study used data from an online survey conducted in April 2020. Details of the survey design and administration are reported elsewhere [20]. Briefly, the sample was recruited among social media users through an online advertisement campaign within Facebook and its affiliated platforms. The eligibility was limited to English-speaking adults aged 18 years and over residing in the United States (US). Participation was voluntary, and participants did not receive any compensation. For this study, we only included participants who provided written informed consent and responded to all behavior- and knowledge-related questions. As a result, a total of 6,518 responses were included in the analysis. The study protocol was reviewed and deemed exempt by the affiliated institution’s Institutional Review Board.

Questionnaires and variables of interest

The questionnaire was designed based on the Health Belief Model (HBM) [21] and the World Health Organization (WHO) survey tool for behavioral insights on COVID-19 [22].

Outcome variable

The outcome of interest was the degree of self-reported engagement with recommended protective behaviors against COVID-19. For this, an index variable was derived from participants’ answers to a set of 13 binary questions. Answers were assessed based on the recommendations on protective behaviors provided by the Centers for Disease Control and Prevention (CDC). While correct answers were assigned a score of 1, answers that do not comply with the CDC recommendations at the time of the survey were scored 0. The sum of scores was used as the overall behavior score (Range: 0–13). Cronbach’s alpha coefficient was used to assess the internal consistency of the summed scores and was 0.7 and was deemed acceptable [23]. All the questions used to construct the outcome variable are provided in the S1 Table.

Predictor variables

The main predictor was the level of knowledge on COVID-19. Similar to the behavior score, we created an index variable for the knowledge score using participants’ answers to a set of 21 binary questions. An overall knowledge score (Range: 0–21) was calculated by assigning 1 for the correct answers and 0 for the incorrect answers. We tested the internal consistency of the questions used to derive the sum of scores and Cronbach’s alpha coefficient was 0.60 for the knowledge score and was deemed acceptable [23]. All the questions used to construct the predictor variable are provided in the S1 Table.

Moderator variables

Participant’s primary source of COVID-19-related information was used as a moderator. The questionnaire asked participants the primary source they used to acquire COVID-19-related information, with the ability to choose only one answer from a list of choices. Primary information sources were categorized into six mutually exclusive categories: (1) family, friends, and colleagues; (2) doctor or medical provider; (3) government or other official sources (e.g., CDC or WHO); (4) traditional media (e.g., newspapers, TV); (5) new media (e.g., social media, web surfing on non-official sources, podcasts); and (6) religious leaders.

Statistical analysis

All statistical analyses were conducted in R (version 3.6.3). The minimal dataset to replicate the analysis is provided in the S1 Dataset. First, we stratified the participants into groups by their primary source of information. We then used chi-square test to evaluate differences in demographic characteristics by group. Pairwise Wilcoxon rank-sum test—a nonparametric alternative to the t test—was used, given the left-skewedness of the scores’ distribution, to compare the distribution of knowledge and behavior scores between each group.

In order to test the hypothesis, ordinary least squares (OLS) linear regression was performed with behavior score as dependent variable, knowledge score as independent variable, and with an interaction term between knowledge score and primary source of information. Sociodemographic factors, including age, sex, employment status, education level, number of sources to acquire COVID-19-related information, and political affiliation, which may affect self-reported engagement with recommended protective behaviors, represented by vector Z in the equation below, were included as covariates [13, 24]. We reported adjusted regression coefficients and corresponding p-values and 95% confidence intervals (CIs).

Results

Description of sample

A total of 6,518 people participated in the survey from April 16 to 21, 2020. Of which, 1,984 (36.8%) participants indicated government or other official sources as their primary source of COVID-19-related information, followed by 1,792 (33.9%) who reported doctor or medical provider (Table 1). Traditional media channels were preferred by 721 (13.6%), and new media by 519 (9.8%) participants, whereas a small fraction (309; 5.8%) cited family, friends, or colleagues. Only three participants named religious leaders as their primary information source. Chi-square test showed almost all demographic variables, including age, sex, race, employment status, and political affiliation (p-value <0.01) were significantly differently distributed between each group, as shown in Table 1. Participants in older age groups preferred doctors or traditional media, whereas those who self-identified as non-white (p-value <0.001), Republican (p-value <0.001), or residing in a rural area (p-value = 0.011) were over-represented among those who indicated social media or family, friends, and colleagues as their primary information source. Standardized residuals of the chi-square test are reported in the S2 Table.

Table 1

Demographics of the study participants (n = 6,518).

		Total (n = 6518)	Doctor or medical provider (n = 1792)	Government or other official sources (e.g. CDC or WHO) (n = 1948)	Traditional media (n = 721)	New media (Social media, web surfing, podcasts, and etc. (n = 519)	Family, friends, and coworkers (n = 309)	Religious leaders (n = 3)	p-value
Sex									<0.001
	Female	3717 (57.6%)	975 (54.8%)	1289 (66.8%)	448 (62.8%)	225 (43.7%)	139 (45.4%)	3 (100.0%)
	Male	2738 (42.4%)	804 (45.2%)	641 (33.2%)	265 (37.2%)	290 (56.3%)	167 (54.6%)	0 (0.0%)
Age group									<0.001
	18–39 years old	1078 (16.5%)	243 (13.6%)	389 (20.0%)	107 (14.8%)	82 (15.8%)	51 (16.5%)	0 (0.0%)
	40–59 years old	2811 (43.1%)	770 (43.0%)	876 (45.0%)	264 (36.6%)	262 (50.5%)	140 (45.3%)	2 (66.7%)
	60+ years old	2629 (40.3%)	779 (43.5%)	683 (35.1%)	350 (48.5%)	175 (33.7%)	118 (38.2%)	1 (33.3%)
Race									0.006
	White, Non-Hispanic	6012 (92.2%)	1675 (93.5%)	1817 (93.3%)	679 (94.2%)	469 (90.4%)	277 (89.6%)	2 (66.7%)
	Non-White	506 (7.8%)	117 (6.5%)	131 (6.7%)	42 (5.8%)	50 (9.6%)	32 (10.4%)	1 (33.3%)
Employment status									<0.001
	Employed	2845 (56.2%)	941 (54.9%)	1096 (58.4%)	354 (51.1%)	300 (61.6%)	153 (53.5%)	1 (50.0%)
	Student/Unpaid work	280 (5.5%)	74 (4.3%)	128 (6.8%)	37 (5.3%)	21 (4.3%)	20 (7.0%)	0 (0.0%)
	Not working/Unemployed	635 (12.5%)	204 (11.9%)	239 (12.7%)	87 (12.6%)	66 (13.6%)	39 (13.6%)	0 (0.0%)
	Retired	1300 (25.7%)	495 (28.9%)	415 (22.1%)	215 (31.0%)	100 (20.5%)	74 (25.9%)	1 (50.0%)
Educational attainment									0.0602
	High school or less	516 (13.9%)	178 (13.8%)	190 (14.0%)	49 (10.8%)	62 (15.9%)	37 (17.3%)	0 (0.0%)
	Some college / Associate’s degree	1720 (46.5%)	626 (48.6%)	613 (45.3%)	198 (43.7%)	177 (45.5%)	105 (49.1%)	1 (100.0%)
	Bachelor’s degree or higher	1463 (39.6%)	484 (37.6%)	551 (40.7%)	206 (45.5%)	150 (38.6%)	72 (33.6%)	0 (0.0%)
Political affiliation									<0.001
	Democrat	1925 (38.3%)	610 (35.7%)	756 (40.4%)	397 (57.9%)	96 (20.1%)	66 (23.2%)	0 (0.0%)
	Republican	1222 (24.3%)	417 (24.4%)	425 (22.7%)	109 (15.9%)	161 (33.8%)	108 (37.9%)	2 (100.0%)
	Other	1072 (21.3%)	382 (22.4%)	403 (21.6%)	98 (14.3%)	132 (27.7%)	57 (20.0%)	0 (0.0%)
	Prefer not to say	809 (16.1%)	299 (17.5%)	286 (15.3%)	82 (12.0%)	88 (18.4%)	54 (18.9%)	0 (0.0%)

The highest knowledge score was observed for participants who used traditional media (Median = 20, IQR 19–21), government or official sources (Median = 20, IQR 19–21), and doctors or medical providers as primary information source (Median = 20, IQR 19–20), followed by those who preferred new media (Median = 19, IQR 18–20) and family, friends, and colleagues (Median = 19, IQR 17–20). A similar trend was observed for the behavior score with less variability across primary information sources.

Regression analysis on the association between level of COVID-19 knowledge and degree of protective behaviors

The main effect model without the interaction term (adjusted R-square = 0.212), and the fully adjusted regression model with the moderator are reported in Table 2 (adjusted R-square = 0.224). In the main effect model, higher level of knowledge score was positively associated with higher degrees of engagement with protective behaviors against COVID-19. When controlled for all covariates, a unit increase in the knowledge score was associated with a 0.273 increase in the behavior score (95% CI: 0.241–0.305, p-value<0.01).

Table 2

Main effect model and the full linear regression model between the COVID-19 knowledge score and the protective behavior score with covariates and the interaction term (n = 3,663).

Variables		Main effect model			Model with interaction term
		Coefficient	95% Confidence interval	p-value	Coefficient	95% Confidence interval	p-value
(Intercept)		2.986	(2.336, 3.637)	<0.001	2.967	(1.915, 4.020)	<0.001
Knowledge score		0.273	(0.241, 0.305)	<0.001	0.275	(0.221, 0.328)	<0.001
Source of information
	Doctor or medical staff	Ref			Ref	-	-
	Government or other official sources (e.g., CDC or WHO)	0.146	(0.023, 0.269)	0.02	2.021	(0.481, 3.561)	0.01
	Traditional media	0.182	(0.010, 0.354)	0.038	2.297	(0.233, 4.361)	0.029
	New media (Social media, web surfing, podcasts, etc.	-0.302	(-0.484, -0.120)	0.001	-2.155	(-3.863, -0.447)	0.013
	Family, friends, and coworkers	-0.299	(-0.532, -0.066)	0.012	-3.174	(-5.094, -1.255)	<0.001
	Religious leader	-2.514	(-5.613, 0.586)	0.112	-2.508	(-5.595, 0.580)	0.111
Political affiliation
	Democrat	Ref			Ref	-	-
	Republican	-0.39	(-0.528, -0.251)	<0.001	-0.393	(-0.531, -0.255)	<0.001
	Other	-0.329	(-0.473, -0.184)	<0.001	-0.335	(-0.478, -0.191)	<0.001
	Prefer not to say	-0.163	(-0.318, -0.008)	0.04	-0.181	(-0.336, -0.026)	0.022
Number of sources		0.158	(0.134, 0.181)	<0.001	0.156	(0.132, 0.179)	<0.001
Age group
	18–39 years old	Ref			Ref
	40–59 years old	0.148	(-0.005, 0.301)	0.058	0.152	(0.000, 0.305)	0.05
	60+ years old	0.182	(0.002, 0.363)	0.047	0.189	(0.009, 0.368)	0.039
Sex
	Female	Ref			Ref	-	-
	Male	-0.502	(-0.610, -0.395)	<0.001	-0.505	(-0.612, -0.398)	<0.001
Educational attainment
	High school degree or lower	Ref			Ref	-	-
	Some college / Associate degree	-0.312	(-0.475, -0.148)	0.005	-0.214	(-0.371, -0.057)	0.007
	Bachelor’s degree or higher	-0.223	(-0.380, -0.066)	<0.001	-0.301	(-0.464, -0.138)	<0.001
Employment status
	Employed	Ref			Ref	-	-
	Student/Unpaid work	0.078	(-0.148, 0.304)	0.499	0.063	(-0.163, 0.288)	0.586
	Not working/Unemployed	0.417	(0.266, 0.569)	<0.001	0.416	(0.265, 0.567)	<0.001
	Retired	0.208	(0.056, 0.360)	0.007	0.199	(0.047, 0.351)	0.01
Interaction term
	Knowledge score * D&M*				Ref	-	-
	Knowledge score * GOV*				-0.096	(-0.175, -0.017)	0.018
	Knowledge score * TRAD*				-0.109	(-0.215, -0.003)	0.044
	Knowledge score * NEWM*				0.1	(0.009, 0.190)	0.031
	Knowledge score * FFC*				0.158	(0.055, 0.262)	0.003

*D&M: Doctors or medical staff/ GOV: Government or other official sources / TRAD: Traditional media / NEWM: New media (Social media, web surfing, podcasts, etc.) / FFC: Family, friends and coworkers.

In the fully adjusted model, all interaction terms between the knowledge score and the primary source of information were significantly associated with the changes in the behavioral score (excluding religious leaders because a coefficient could not be derived due to the limited sample size (n = 3)). In summary, while the behavior score was positively associated with the knowledge score (adjusted coefficient 0.275, p-value <0.01), when all covariates were held constant, the association was significantly stronger when the primary source of information was social media, podcasts or unofficial websites (interaction term coefficient 0.1, p-value = 0.031), or family, friends and colleagues (interaction term coefficient 0.158, p-value <0.01), in comparison to when the primary source was through doctor or medical staff (reference category). On the contrary, the association was significantly weaker when the primary source of information was traditional media (interaction term coefficient -0.109, p-value = 0.044), or the government or other official sources (interaction term coefficient -0.096, p-value = 0.018). For all primary information sources, the increase in the behavioral score was larger with the increasing number of sources used (adjusted coefficient 0.156, 95% CI: 0.132–0.179, p-value<0.01). The F-test showed that model with interaction term fitted the data significantly better than the main effect model (p-value <0.01).

The visualization of this result as a fitted linear plot of the association between COVID-19 knowledge and behavior with the modifying effect of primary information source is shown in Fig 1. In this figure, the slope represents the association between knowledge and behavior. The varying slopes by different primary information source, as represented by different colors in the legend, corresponds to the effect modification as shown by the significant coefficients for interaction terms in Table 2.

Fig 1

Fitted linear model for the association between COVID-19 knowledge (x-axis) and protective behaviors (y-axis) by primary information source of COVID-19.

Discussion

Our study provides one of the first empirical evidence on effect modification by primary source of information on the association between knowledge and engagement with protective behaviors against COVID-19. The findings can be summarized in three points: First, the primary source of COVID-19 knowledge differs across sociodemographic subgroups, which may result in varying levels of knowledge related to COVID-19. Distinctively, participants with the lowest level of knowledge preferred informal sources, such as social media and family, friends and colleagues, as their primary source of information. Second, among those with high levels of knowledge, the primary source of information did not predict their protective behaviors. Lastly, the primary source of information significantly moderated the association between knowledge and behavior, and analyses of simple slopes revealed significant differences by primary information source (Fig 1). While our study design is cross-sectional and therefore does not allow us to draw any inference on causality, the results suggest that different communication media may deliver the same information in distinctive ways, which may lead to differing levels of knowledge across individuals and translate into different levels of engagement with behaviors.

This study brings particular attention to two sources of information: 1) online media sources, including social media and websites other than those of official governments or international organizations, and 2) informal communication between family, friends, and colleagues. In contrast to other information sources, acquisition of knowledge from both of these sources occurs interactively and informally. Not only were both a preferred source of COVID-19-related information among the study participants at the bottom quartile of the knowledge score distribution, but also these sources were associated with a higher increase in the level of engagement with protective behaviors given the same unit increase in the knowledge score, as demonstrated by steeper slopes in Fig 1. These two contrasting findings from our study suggest that these sources, when leveraged well, hold potential to empower people. By the same token, when misused, these information sources may present a health threat. The mechanism of how information presented through these informal sources can potentially be associated with behavior change could be an area of future research. Due to the limitation of the cross-sectional study design, it is not within the scope of the current study to test whether these two sources had indeed diminished the level of both correct knowledge and engagement with protective behaviors. Regardless, the results suggest that, when these sources are leveraged appropriately to improve knowledge, the translation of knowledge to behavior among the participants who primarily uses these sources could be effective.

In summary, primary sources of information may be partially accountable for varying levels of COVID-19-related knowledge, reflecting different sociodemographic characteristics of the main audience of each source, and its heterogeneous associations with individuals’ engagement with protective behaviors against COVID-19. Our results suggest that the primary source of information may act as a moderator in the pathway from knowledge to behavior, and sources of information and the manner in which each source conveys information to the public could serve as the tangible target of intervention for improved risk communication.

The study has a number of limitations that leave room for further research. First, the study design introduces a number of biases. Despite the large sample size, the study sample, drawn from nonprobability convenience sampling via social media platforms affiliated with Facebook, is not representative of the US population [20]. While our sample showed a balanced distribution of participants from every US state, age group, and type of residence, certain key subpopulations are underrepresented in the study sample. For example, the survey did not include the people without access to Internet or social media account affiliated with Facebook. While around 70% of the US population are estimated to have Facebook accounts, and among them, 75% use Facebook on a daily basis [25], it is also estimated that about 20% of the US households do not have Internet at home [26]. In addition, more than 40 million adults in the US are of foreign origin, and almost one third of them do not speak English well, thus would be unable to participate in our study [27]. Our sample of participants was also overwhelmingly non-Hispanic white. Overall, high scores of COVID-19-related knowledge observed in this study could have been due to the sampling strategy, which most likely attracted people who were more interested in COVID-19. Meanwhile, the small sample size of those who reported seeking COVID-19-related information primarily from religious leaders could be due to the lack of engagement of this specific group in social media platforms in general [28]. To partially overcome this limitation, we made substantial efforts to oversample from potentially under-represented groups, including men and racial and ethnic minorities [20]. However, it is important to note that, due to all of the aforementioned factors, the findings from our study are not generalizable to the US population as a whole. We still strongly believe that our findings hold important implications for designing risk communication strategies, targeting particularly those who use social media platforms as their primary source of information.

Second, level of engagement with protective behaviors was based on self-report, which is subject to response bias. Although presented in random order, the questions on engagement with protective behaviors were relatively simple and dichotomized, and it was straightforward to guess the correct answers; this may have resulted in over-estimation of the level of engagement in specific subgroups. Moreover, given the nature of the observational design, our study is subject to many known and unknown confounders, which should be addressed by quasi-experimental or experimental studies.

Additionally, measurements of the level of knowledge and behavioral engagement were done via questions developed and used in previous surveys [20], but we used unvalidated scales based on the sum of correct answers to the survey questions without being able to test their validity. Internal consistency of the questions was tested and shown to be reliable for the behavior score, but less reliable for the knowledge score. Future studies using validated scales to measure behaviors and knowledge are needed.

Lastly, overall high knowledge scores among the study participants and the resulting lack of variability in the independent variable may be regarded as a limitation to the analysis. A supplementary qualitative study of the participants could facilitate a deeper understanding of the association between knowledge and behaviors in order to better inform risk communication strategies.

Conclusions

As the COVID-19 pandemic persists over a year since its first detection, more and more people are feeling fatigued about continuous engagement with protective measures [29, 30]. However, it is crucial to maintain public vigilance and a collectively high level of engagement with protective behaviors in order to prevent further spread of the virus, particularly because of the slow progress with COVID-19 vaccine implementation and the emergence of several variants of the COVID-19 virus [31]. Even small deficiencies in protective behavior compliance can result in significant community spread, as evidenced by the second and third waves of COVID-19 cases in several countries, including the US [32, 33]. With the concurrent infodemic severely hindering the ability of health authorities to convey timely and accurate information, customizing risk communications with appropriate prioritization of high-risk populations and information platforms with stronger capacity to promote behavior change (such as online or interpersonal information sources) is of absolute importance. In order to effectively promote community-level protective behaviors, it is necessary to step out of the traditional way of mass-scale and didactic communication and proactively reach out and engage through the channels where people are more willing to seek information.

Questionnaire used in the survey.

(DOCX)

Click here for additional data file.

Standardized residuals of chi-square test performed on the demographics of the study participants (n = 6,518).

(DOCX)

Click here for additional data file.

Study dataset.

(CSV)

Click here for additional data file.

8 Sep 2021

PONE-D-21-24232Impact of COVID-19-Related Knowledge on Protective Behaviors: The Moderating Role of Primary Sources of InformationPLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Review for PONE-D-21-24232

The study explores the moderating effects of information sources on the association between COVID-19 knowledge and behavior. The study is interesting and relevant for COVID-19-related interventions but also the field of health communication more generally. I have several suggestions that I believe would help make the manuscript clearer to the reader. Otherwise, I believe this is a good paper.

1. The introduction seems to focus on the effects of information sources on health behavior in the context of COVID-19. I think the study would benefit from a review of this topic in other contexts; if none exist, this could also be emphasized as a further contribution of the current study.

2. On page 2 line 55, “6-53% of participants…” this seems like a wide range, and it is unclear what the numbers refer to. Are they different numbers for different platforms?

3. Usually, sample size and reliabilities are mentioned in the Methods section rather than the results. The reliabilities also seem to be rather weak, perhaps removing some items could improve them?

4. On page 4 line 82, what is HBM?

5. On page 4, line 89, by “incorrect” do you mean not meeting CDC guidelines? This is not “incorrect” in the usual sense of the word. I also think the authors should add the scales as an appendix or at least give an example of the items because right now it is difficult to understand what behaviors are included.

6. I suggest the authors use a Rasch model to score the items rather than simple dichotomous scores because different items should contribute differently to the final score. A person who refrains from leaving their house at all but does not wash their hands often is different from a person who washes their hands but does not wear a mask when meeting others.

7. Why did the authors decide to have the information sources mutually exclusive? Since the authors have data on the number of sources, it would have made more sense to allow participants to choose several sources.

8. Why was it not possible to use a t-test?

9. The authors provide good details on their analysis.

10. I suggest adding standardized residuals to the chi-square analyses so it is clearer what categories were more extremely represented in each group.

11. On page 13, lines 193-195, “the primary source of information did not significantly moderate the association between knowledge and behaviors among the population with high levels of knowledge on COVID-19” – I understand what the authors wanted to say but the phrasing here is wrong. Of course there was no moderation among people with high levels of knowledge, as there is no variance among them. Should be “among those with high levels of knowledge, the primary source of information did not predict behavior”. Also, since the authors did not look at the population but at a sample, they should not refer to a population.

12. The authors conclude that the information sources lead to differing levels of knowledge which lead to different behaviors, and suggest that misusing certain sources can be dangerous. It seems to me that it is more likely that people who wanted to seek information were active about it and talked to doctors or government sources, whereas others heard about COVID-19 from sources they engage with anyway, namely, social media and personal relationships. Those who were interested in COVID-19 due to fear or other reasons looked into the topic, gained a lot of knowledge, and were more likely to engage with the recommended behaviors regardless of their source. I think the authors should be more careful when stating their conclusions.

13. While underrepresented demographic groups are certainly a limitation, the issue of representativeness of social media users seems more relevant here. The sample included Facebook users who are likely interested in COVID-19, otherwise, they wouldn’t have engaged with the study (this explains the high knowledge scores). This is likely to affect the results (e.g., people who turn to religious figures are less likely to participate, and if they do they probably do not represent other people who consult with religious authorities).

Reviewer #2: This study shows the important role of COVID-19 information sources in affecting 30 people’s engagement in recommended protective behaviors. The idea is well described theorotically and analytically.

Reviewer #3: While the findings of the study are interesting and comprehensive, one major shortcoming of the statistical analysis is that the authors have used inferential methods on the basis of a non-probability sample (convenience sample). As inferences are often drawn / valid only if a probability sample is used, so the authors need to justify it. If authors have followed a previous work where inferences have been drawn on the basis of a non-probability sample, they must cite it. The authors must clear this point.

Moreover, the abstract has been divided into sub-sections. I suggest the authors to write a brief abstract as a single section. No need to divide the abstract into sub-sections.

Further, in the text, citations have been given using parenthesis. Use square brackets for citations.

Reviewer #4: The paper addresses a "hot" topic in the context of the new coronavirus pandemic. The introduction provides even some information related to the literature review, even though the authors should try to better present the current state in a more critical manner. The methodology is quite simple as the data used in the paper is taken by the authors from external sources. The authors provide information related to predictor variables and moderator variables, but no scheme of the proposed model is provided. In my opinion, with given information, it is hard for the work to be reproduced or adapted to similar situations. The results section contains little information strictly related to the subject. The authors provide a long table containing descriptive statistics related to the participants to the study, with no real connection to the results as the results are very brief with respect to this information. Figure 1 is hard to understand - can you please explain it more in the main body of the paper? The moderator variable used in the study is a variable from a list. In most of the cases, it might happen that a person can be connected to multiple sources of information, each of them bringing its own contribution to the result. I think that a more-in-depth analysis is needed.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Rahim Alhamzawi

Reviewer #3: No

Reviewer #4: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: Review Report PONE-D-21-24232.docx

Click here for additional data file.

30 Sep 2021

Response to Reviewer #1

The study explores the moderating effects of information sources on the association between COVID-19 knowledge and behavior. The study is interesting and relevant for COVID-19-related interventions but also the field of health communication more generally. I have several suggestions that I believe would help make the manuscript clearer to the reader. Otherwise, I believe this is a good paper.

-> Thank you for your thoughtful feedback. We hope the revisions improved the quality of our manuscript and addressed all the points you raised.

1. The introduction seems to focus on the effects of information sources on health behavior in the context of COVID-19. I think the study would benefit from a review of this topic in other contexts; if none exist, this could also be emphasized as a further contribution of the current study.

-> Thank you for this suggestion. We updated the introduction with additional paragraph (line 71 – 76 to briefly cover the context prior to COVID-19 pandemic.

2. On page 2 line 55, “6-53% of participants…” this seems like a wide range, and it is unclear what the numbers refer to. Are they different numbers for different platforms.

-> Thank you for pointing this out. We corrected the sentence to provide examples of varying social media and online search platform usage in 6 mentioned countries (lines 61-64). We hope the revised sentence reads better.

3. Usually, sample size and reliabilities are mentioned in the Methods section rather than the results. The reliabilities also seem to be rather weak, perhaps removing some items could improve them?

-> Thank you for this suggestion. We clarified the sample size (lines 90-91) and the measurement of internal consistency (lines 104-105 and lines 112-113) in the Methods section. For the reliability of the questions used for the predictor variable, we referenced Hulin et al.’s widely referenced work (https://www.jstor.org/stable/1480474?refreqid=excelsior%3Afbdac5eed827fb5efe04e1e fa5eb9616&seq=1#metadata_info_tab_contents), which considers the calculated value of 0.6 as acceptable.

4. On page 4 line 82, what is HBM?

-> Thank you for pointing this out. We spelled out the Health Belief Model and added an appropriate reference introducing the HBM (line 95).

5. On page 4, line 89, by “incorrect” do you mean not meeting CDC guidelines? This is not “incorrect” in the usual sense of the word. I also think the authors should add the scales as an appendix or at least give an example of the items because right now it is difficult to understand what behaviors are included.

-> Thank you for this suggestion. We updated the term “incorrect answers” to “answers that does not comply with the CDC recommendations” in order to clarify (lines 101-104). We also presented the questions used to assess knowledge and behaviors in the Supplementary Material file (Table S1).

6. I suggest the authors use a Rasch model to score the items rather than simple dichotomous scores because different items should contribute differently to the final score. A person who refrains from leaving their house at all but does not wash their hands often is different from a person who washes their hands but does not wear a mask when meeting others.

-> Thank you for this suggestion. We appreciate the point you raised about how factor loading may be necessary to capture the different degrees of protection each behavior can exercise for an individual. Following your suggestion, we first used the Rasch model to understand the sensitivity to one’s ability and assess the misfit between persons and items, using the infit and its acceptable range of 0.75 and 1.33 (https://bookdown.org/ dkatz/Rasch_Biome/Rasch.html# optional---visualizing-item-fit). After running the model, we confirmed that the fit is consistent with the fit for the model utilizing the simple sum (see plots in the attached rebuttal letter).

7. Why did the authors decide to have the information sources mutually exclusive? Since the authors have data on the number of sources, it would have made more sense to allow participants to choose several sources.

-> Thank you for this question. Our main hypothesis was that the “primary” source of information plays a significant moderating role in the relationship between knowledge and behavior. Also, the questionnaire was designed to inquire participants’ “primary” source of information, rather than the exhaustive list of all information sources used. However, in order to control for the confounding role of multiple information sources used by participants, we added a continuous variable on “total number of sources used” as a covariate in the analysis.

8. Why was it not possible to use a t-test?

-> Thank you for this question. We used Wilcoxon rank sum test instead of t-test because t-test is a parametric test which requires samples meet certain pre-requirements including normality, equal variances, and independence. For both knowledge and behavior scores, the distribution was left skewed thus not meeting the normality assumption. Wilcoxon rank sum test is a widely-accepted non-parametric alternative test to t-test, which can be used when the sample distribution is not normal (https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-12-78).

9. The authors provide good details on their analysis.

-> Thank you for this positive feedback.

10. I suggest adding standardized residuals to the chi-square analyses so it is clearer what categories were more extremely represented in each group.

-> Thank you for this suggestion. We reported the standardized residuals in the Supporting Information (Table S2) and indicated this information in the manuscript (line 159).

11. On page 13, lines 193-195, “the primary source of information did not significantly moderate the association between knowledge and behaviors among the population with high levels of knowledge on COVID-19” – I understand what the authors wanted to say but the phrasing here is wrong. Of course there was no moderation among people with high levels of knowledge, as there is no variance among them. Should be “among those with high levels of knowledge, the primary source of information did not predict behavior”. Also, since the authors did not look at the population but at a sample, they should not refer to a population.

-> Thank you for this important comment. The sentence is now revised based on your input (line 220). We also checked all the sentences in the manuscript that used the word “population” and change the word to either “respondents” or “people” depending on the context so that it does not imply any inference to the general population.

12. The authors conclude that the information sources lead to differing levels of knowledge which lead to different behaviors, and suggest that misusing certain sources can be dangerous. It seems to me that it is more likely that people who wanted to seek information were active about it and talked to doctors or government sources, whereas others heard about COVID-19 from sources they engage with anyway, namely, social media and personal relationships. Those who were interested in COVID-19 due to fear or other reasons looked into the topic, gained a lot of knowledge, and were more likely to engage with the recommended behaviors regardless of their source. I think the authors should be more careful when stating their conclusions.

-> Thank you for this comment. We made revisions throughout the Discussion section (lines 245-291) to present the findings more conservatively.

13. While underrepresented demographic groups are certainly a limitation, the issue of representativeness of social media users seems more relevant here. The sample included Facebook users who are likely interested in COVID-19, otherwise, they wouldn’t have engaged with the study (this explains the high knowledge scores). This is likely to affect the results (e.g., people who turn to religious figures are less likely to participate, and if they do they probably do not represent other people who consult with religious authorities).

-> Thank you for this comment. We tried to improve our discussion by elaborating more on the limitation of the sampling strategy, including all the points you raised (lines 252 – 273).

Response to Reviewer #2

1. This study shows the important role of COVID-19 information sources in affecting 30 people’s engagement in recommended protective behaviors. The idea is well described theorotically and analytically.

-> Thank you for this positive feedback!

Response to Reviewer #3

1. While the findings of the study are interesting and comprehensive, one major shortcoming of the statistical analysis is that the authors have used inferential methods on the basis of a non-probability sample (convenience sample). As inferences are often drawn / valid only if a probability sample is used, so the authors need to justify it. If authors have followed a previous work where inferences have been drawn on the basis of a non-probability sample, they must cite it. The authors must clear this point.

->Thank you for your comment. We improve the discussion by further emphasizing the limitation of the sampling strategy, specifically including the points you raised in your comment (lines 252 – 273). We also reviewed the entire manuscript to make sure that there is no inference made to the general US population based on our findings. We believe that our findings are relevant in a way that it suggests policymakers to re-consider their risk communication strategies, especially for those who use social media platforms as their primary source of information. We hope that our revisions sufficiently address your concerns.

2. Moreover, the abstract has been divided into sub-sections. I suggest the authors to write a brief abstract as a single section. No need to divide the abstract into sub-sections.

->We followed your suggestion and removed the subsections in the abstract.

3. Further, in the text, citations have been given using parenthesis. Use square brackets for citations.

->We updated the citations using EndNote’s PLoS output style to comply with the journal requirements.

Response to Reviewer #4

1. The paper addresses a "hot" topic in the context of the new coronavirus pandemic. The introduction provides even some information related to the literature review, even though the authors should try to better present the current state in a more critical manner.

-> Thank you for this comment. We updated the introduction with additional paragraph (line 71 – 76) in order to further highlight the timeliness and significance of this paper. We hope these revisions help presenting the context in a more critical manner.

2. The methodology is quite simple as the data used in the paper is taken by the authors from external sources. The authors provide information related to predictor variables and moderator variables, but no scheme of the proposed model is provided. In my opinion, with given information, it is hard for the work to be reproduced or adapted to similar situations.

-> Thank you for this comment. In order to enhance the reproducibility, we added the equation for the regression analysis in the manuscript (lines 141-142) and included the survey questions used for the predictor and outcome variables in the Supporting Information file (Table S1).

3. The results section contains little information strictly related to the subject. The authors provide a long table containing descriptive statistics related to the participants to the study, with no real connection to the results as the results are very brief with respect to this information.

-> Thank you for this comment. We included Table 1 to comply with the standard of quantitative research papers suggested by APA (https://apastyle.apa.org/jars/quant-table-1.pdf). Following your comment, we excluded the variables that are not included in the final regression model from Table 1. Results pertinent to the effect modification is presented in Table 2, Figure 1, and the paragraph that is presented in lines 185-205.

4. Figure 1 is hard to understand - can you please explain it more in the main body of the paper?

-> Thank you for this comment. We provided an explanation of Figure 1 in the Results section, lines 200-205.

5. The moderator variable used in the study is a variable from a list. In most of the cases, it might happen that a person can be connected to multiple sources of information, each of them bringing its own contribution to the result. I think that a more-in-depth analysis is needed.

-> Thank you for this comment. In this study we aimed to test the hypothesis that the “primary” source of information plays a significant moderating role in the association between knowledge and behavior. The questionnaire inquired the participants’ “primary” source of information. And, we controlled for the confounding role of the multiple sources of information used by respondents by adding a continuous variable on the “total number of sources used” as a covariate in the analysis. We agree that future studies are warranted to explore the complex relationship between multiple sources of information and its role in moderating the association between knowledge and behavior.

Submitted filename: Revision_PONE-D-21-24232_final.docx

Click here for additional data file.

25 Oct 2021

PONE-D-21-24232R1Impact of COVID-19-Related Knowledge on Protective Behaviors: The Moderating Role of Primary Sources of InformationPLOS ONE

Dear Dr. Tozan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The revised version of the paper has considered the reviewers' comments. There are still some aspects to be addressed as it results from the reviewers' comments. Please consider them when submitting the revised version.

Please submit your revised manuscript by Dec 09 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have successfully addressed most of my comments. I do have a few remaining questions.

1. The authors focus on primary information sources rather than number or type of sources. However, their introduction section does not mention primary information source (but rather, social media as a complementary source). I think the authors should explain why they chose to only focus on one source, what is the logical or theoretical justification for the decision.

2. Thank you for conducting the Rasch analysis. While your results support the use of a Rasch scale over summary scores given the great fit statistics, I understand it might be too difficult to change the analyses at this stage.

3. I understand the purpose of a Wilcoxon test, but the authors did not provide evidence that the data violate the assumptions of a t-test. They should add the sentence about the scores being left-skewed to the manusctipt.

4. The tables do not follow APA style.

Reviewer #3: COVID-19 is a global issue and these types of research studies are badly needed. I found this research study very interesting and worth publishable.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

27 Oct 2021

Reviewer #1: The authors have successfully addressed most of my comments. I do have a few remaining questions.

1. The authors focus on primary information sources rather than number or type of sources. However, their introduction section does not mention primary information source (but rather, social media as a complementary source). I think the authors should explain why they chose to only focus on one source, what is the logical or theoretical justification for the decision.

-> Thank you for this comment. We expanded the text in Introduction to explain further and justify the focus on primary source of information along with references from the published literature. Please kindly refer to lines 68-72 and lines 82-84.

2. Thank you for conducting the Rasch analysis. While your results support the use of a Rasch scale over summary scores given the great fit statistics, I understand it might be too difficult to change the analyses at this stage.

-> Thank you for this feedback.

3. I understand the purpose of a Wilcoxon test, but the authors did not provide evidence that the data violate the assumptions of a t-test. They should add the sentence about the scores being left-skewed to the manuscript.

-> Thank you for this feedback. We added this explanation in lines 136-137 as follows: “Pairwise Wilcoxon rank-sum test—a nonparametric alternative to the t test—was used, given the left-skewedness of the scores distribution, to compare the distribution of knowledge and behavior scores between each group.”

4. The tables do not follow APA style.

-> Thank you for this feedback. We followed the SAMPL guideline (https://www.equator-network.org/wp-content/uploads/2013/03/SAMPL-Guidelines-3-13-13.pdf) for statistical reporting and PLOS ONE’s guideline (https://journals.plos.org/plosone/s/tables) for table formatting. We re-looked at both references to ensure correct reporting format and style and made following additional updates on both Table 1 and Table 2:

1) We added extra column, instead of using indents, to differentiate the variable name and subsequent category names.

2) We reported all the p-values equal to or greater than 0.001 as equalities and only reported values under 0.001 as inequalities.

3) Following your feedback, we formatted tables with the horizontal lines only to be compliant with APA style (https://apastyle.apa.org/style-grammar-guidelines/tables-figures/tables)

4) To improve legibility, we used bold text for the variable names and primary column titles.

Reviewer #3: COVID-19 is a global issue and these types of research studies are badly needed. I found this research study very interesting and worth publishable.

->We thank you for your endorsement.

Submitted filename: Revision2_PONE-D-21-24232_final.docx

Click here for additional data file.

15 Nov 2021

Impact of COVID-19-Related Knowledge on Protective Behaviors: The Moderating Role of Primary Sources of Information

PONE-D-21-24232R2

Dear Dr. Tozan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Camelia Delcea

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: No

17 Nov 2021

PONE-D-21-24232R2

Impact of COVID-19-related knowledge on protective behaviors: The moderating role of primary sources of information

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