¹Graduate Program in Integrated Clinical Dentistry, State University of Maringá (UEM), Maringá, Brazil
Daniela Fernandes Ceron¹, Beatriz Zamboni Martins Panucci¹*, Ana Carolina Lobo¹, Maria Cecília Bruningn2, Gabriela Cristina Santin3
2Undergraduate Program in Dentistry, UniCesumar – Centro Universitário de Maringá, Maringá, Brazil
3Department of Dentistry, State University of Maringá (UEM), Maringá, Brazil
*Corresponding author:
Beatriz Zamboni Martins Panucci
Email: beeatrizamboni@gmail.com
Phone: +55 (44) 99957 − 6553
Graduate Program in Integrated Clinical Dentistry, State University of Maringá (UEM), Maringá, Brazil
Media Health Literacy and Discernment of Fake News in Dentistry: A Cross-Sectional Study
A
Abstract
Background
Media health literacy (MHL) has been increasingly recognized as an important skill in identifying false or misleading health information on the internet. Although this association has been explored in medicine and public health, few studies have focused specifically on dentistry. The aim of the present study was to investigate the association between MHL levels and the ability to discern fake news in the field of dentistry.
Methods
A
A cross-sectional study was conducted with 240 participants recruited using a snowball sampling strategy. The participants answered a sociodemographic questionnaire, the eHealth Literacy Scale (eHEALS), and a custom-designed fake news questionnaire addressing dental topics. Descriptive analysis, the Mann–Whitney test, and linear regression models were used to identify associations between variables.
Results
Among the 240 participants, 75.8% were female and 65.9% were between 24 and 39 years of age. Most participants had a monthly income more than R$4,000 and 49.6% held postgraduate degrees. The median eHEALS score was 32 (maximum of 40), whereas the median fake news discernment score was 3 out of 8. Higher MHL levels (eHEALS scores) were significantly associated with having a postgraduate degree, income over R$8,000, and being a healthcare professional, particularly a dentist (p < 0.05). The regression analysis showed that occupation and eHEALS score were significant predictors of the ability to discern fake news.
Conclusions
Higher media health literacy is associated with a greater ability to recognize misinformation in dentistry. Even among dental professionals, gaps in the critical assessment of online content persist. Educational strategies aimed at improving MHL should be prioritized to enhance public and professional discernment, mitigate the spread of fake news, and promote informed decision-making in oral health.
Keywords:
Media Health Literacy
Fake News
Dentistry
Health Communication
eHealth
Critical Thinking
Health Misinformation
Digital Literacy
Dental Professionals
Public Health
A
A
Background
The COVID-19 pandemic declared in 2020 led to a significant increase in access to and the search for health-related information on the internet. This phenomenon resulted in an environment saturated with data (often contradictory or incorrect), characterizing what the literature has denominated an infodemic—an excess of information, precise and otherwise, that makes the identification of reliable sources and evidence-based guidance difficult (Garcia and Duarte, 2020; Raman et al., 2024).
In this situation, critical thinking is essential to the determination of valid information, especially when considering health. The ability to locate, understand, assess, and apply information available in digital environments is denominated media health literacy (MHL), which is an extension of the traditional health literacy concept that includes digital skills focused on a critical analysis of content posted on social media and online platforms (Smith and Magnani, 2019; Do et al., 2020).
Previous studies have demonstrated that higher levels of MHL are associated with factors such as a higher education level, higher income, younger age, being female, and working in the healthcare field (Hanna et al., 2017; Gazibara et al., 2019; Baek et al., 2021; Hakeem et al., 2023). Despite the growing volume of misinformation disseminated online, studies specifically investigating the association between this type of MHL and the ability to identify fake news in dentistry are scarce (Valizadeh-Haghi and Rahmatizadeh, 2018; Lotto et al., 2020; Lotto et al., 2023).
Misinformation in the dental field can significantly compromise the quality of information received by the public, affecting clinical decision making, adherence to treatment, and even the relationship between dentists and patients. Fake news about oral health, such as that related to the use of fluoride, the effects of teeth whitening products, or the safety of procedures during pregnancy, has become increasingly common on social media and messaging apps (Al-Amad and Amal, 2021; Da Silva and Walmsley, 2019).
Recent studies indicate that individuals with higher MHL tend to be less susceptible to the influence of unverified information or information disseminated by digital influencers, especially with regards to subjects related to oral health (Collet et al., 2024). In Brazil, instruments such as the Digital Health Literacy Instrument (DHLI-BrA) have been validated and applied to adolescents, revealing the importance of assessing this skill in different subpopulations and cultural contexts (Barbosa et al., 2024).
Given this scenario, the aim of the present study was to investigate the association between MHL level and the ability to discern incorrect information related to dentistry, classified as fake news. The hypothesis is that individuals with a higher level of MHL, especially those working in the healthcare field, have a greater ability to recognize false information.
Methods
Study design and setting
A cross-sectional study with a quantitative, analytical approach was conducted between November and December 2023 with the aid of an electronic questionnaire. The instrument was made available through the Google Forms® platform and disseminated through the lead author’s social media channels as well as through instant messaging groups composed of academics and healthcare providers. The sampling method was non-probabilistic and employed the “snowball” method, as participants were encouraged to share the link to the questionnaire with other potential respondents.
Participants
Individuals older than 18 years of age working or training in the healthcare field with internet access and fluency in Portuguese were included.
A
Participation was voluntary and the volunteers provided informed consent digitally before beginning the questionnaire. The exclusion criteria were incomplete questionnaires and the submission of duplicate answers. This study received approval from the Human Research Ethics Committee of
Universidade Estadual de Maringá (approval number: COPEP: 5.614.203) and was conducted in accordance with the Declaration of Helsinki.
Data collection instruments
The questionnaire was composed of three main components:
1.
Sociodemographic data: age, sex, schooling level, occupation, and frequency of use of social media.
2.
Assessment of media health literacy: an instrument adapted from tools previously validated in international literature was used to measure the degree of MHL, focusing on critical capacity with regards to health content in digital environments.
3.
Fake news discernment capacity: assessed by presenting a set of statements related to dentistry, previously identified as true or false based on scientific evidence. The participants were expected to indicate whether they considered each statement to be true or false or whether they did not know.
The total score was calculated according to previously defined criteria, enabling categorization into different levels of MHL and accuracy in recognizing fake news.
Statistical analysis
The data were exported to IBM SPSS Statistics® version 28.0. Descriptive statistics were used to characterize the sample, with the calculation of absolute and relative frequencies for categorical variables as well as measures of central tendency and dispersion for numerical variables. For the inferential analysis, Pearson’s chi-square test was used to determine associations between categorical variables and the Mann-Whitney test was used to compare medians, with the significance level set at 5% (p < 0.05).
Results
Two hundred forty individuals participated in this study, 75.8% of whom were women and 65.9% were between 24 and 39 years of age. Most participants reported working in the health field (56.3%, n = 135), 82 of whom were dentists. A total of 49.6% had postgraduate degrees and 76.3% reported a monthly income greater than R$4,000 (Table 1).
Table 1
Distribution of participants according to socioeconomic characteristics and occupation (n = 240).
|
Variable
|
Category
|
n (%)
|
|
Sex
|
Female
|
182 (75.8)
|
|
Male
Others
|
58 (24.2)
0 (0.0)
|
|
Age
|
Up to 23 years
|
28 (11.7)
|
|
24 to 29 years
|
77 (32.1)
|
|
30 to 39 years
|
81 (33.8)
|
|
40 years or older
|
54 (22.5)
|
|
Schooling
|
High school
|
26 (10.8)
|
|
University/college
|
95 (39.6)
|
|
Graduate school
|
119 (49.6)
|
|
Monthly family income
|
Up to R$4000
|
57 (23.8)
|
|
R$4001 to R$8000
|
93 (38.8)
|
|
More than R$8000
|
90 (37.5)
|
|
Occupation
|
“I do not work in the health field”
|
105 (43.8)
|
|
“I am not a dentist, but I work in the health field”
|
53 (22.1)
|
|
“I am a dentist”
|
82 (34.2)
|
The analysis of responses to the questionnaire on fake news in dentistry revealed that most participants answered correctly to most of the statements. The frequency of sharing incorrect information was also determined (Table 2).
Table 2
Distribution of participants according to answers on fake news questionnaire and sharing of information (n = 240)
|
Item
|
Categories
|
n (%)
|
Sharing
|
No sharing
|
|
Toothpaste with fluoride < 3 years of age
|
True
|
50 (25.0)
|
31 (62.0)
|
19 (38.0)
|
|
False
|
130 (54.2)
|
35 (26.9)
|
95 (73.1)
|
|
Uncertain
|
60 (20.8)
|
11 (18.3)
|
49 (81.7)
|
|
Fluoride and pineal gland
|
True
|
40 (16.7)
|
23 (57.5)
|
17 (42.5)
|
|
False
|
95 (39.6)
|
16 (16.8)
|
79 (83.2)
|
|
Uncertain
|
105 (43.8)
|
6 (5.7)
|
99 (94.3)
|
|
Periodontitis and premature birth
|
True
|
137 (57.1)
|
111 (81.0)
|
26 (19.0)
|
|
False
|
40 (16.7)
|
4 (10.0)
|
36 (90.0)
|
|
Uncertain
|
63 (26.3)
|
12 (19.0)
|
51 (81.0)
|
|
Coffee consumption and whitening
|
True
|
130 (54.2)
|
112 (86.2)
|
18 (13.8)
|
|
False
|
94 (39.2)
|
34 (36.2)
|
60 (63.8)
|
|
Uncertain
|
16 (6.7)
|
0 (0.0)
|
16 (100.0)
|
The median score on the eHeals instrument used to assess media health literacy (MHL) was 32 points (maximum of 40) and the median score on the fake news questionnaire was 3 points out of a total of 8. Both had non-normal distribution according to the Shapiro-Wilk test (p < 0.001) (Table 3).
Table 3
Medians scores on fake news and eHeals questionnaires (n = 240).
| |
Min.
|
25th percentile 25
|
Mediana
|
75th percentile
|
Max.
|
|
Fake News
|
0
|
2
|
3
|
6
|
8
|
|
eHeals
|
10
|
27,8
|
32
|
35
|
40
|
The bivariate analysis demonstrated that participants with a postgraduate degree, income above R$8,000, and healthcare providers had significantly higher MHL scores (Table 4).
Table 4
Bivariate Poisson regression of socioeconomic characteristics and eHeals score.
|
Variables
|
Category
|
Median
|
PR
|
CI (95%)
|
p-value*
|
|
Sex
|
Female
|
32
|
1.02
|
0.956–1.09
|
0.539
|
|
Male
|
32
|
|
|
|
|
Age
|
40 years or older (ref.)
|
32.5
|
|
|
|
|
Up to 23 years
|
32
|
1.09
|
0.982-1.20
|
0.230
|
|
24 to 29 years
|
32
|
1.06
|
0.983–1.15
|
|
|
30 to 39 years
|
31.0
|
1.01
|
0.939-1.10
|
|
|
Schooling
|
Up to high school (ref.)
|
30
|
|
|
|
|
University/college
|
32
|
1.09
|
0.986-1.20
|
0.008
|
|
Graduate school
|
32
|
1.15
|
1.046–1.27
|
|
|
Income
|
Up to R$4000 (ref.)
|
30
|
|
|
|
|
R$4001 to R$8000
|
32
|
1.06
|
0.986–1.14
|
0.092
|
|
> R$8000
|
32
|
1.08
|
1.008–1.17
|
|
|
Occupation
|
Other fields (ref.)
|
29
|
|
|
|
|
Health field
|
32
|
1.16
|
1.08–1.24
|
< 0.001
|
|
Dentist
|
35
|
1.28
|
1.20–1.35
|
|
Legend: PR = prevalence ratio; CI = confidence interval; ref. = reference.
Schooling, monthly income, occupation, and eHeals score were all significantly associated with the score on the fake news questionnaire (Table 5)
Table 5
Bivariate Poisson regression of socioeconomic characteristics, eHeals score, and score on fake news questionnaires.
|
Variables
|
Category
|
Median
|
PR
|
CI (95%)
|
p-value*
|
|
Sex
|
Female
|
3.00
|
0.886
|
0.721–1.08
|
0.233
|
|
Male
|
3.00
|
|
Age
|
40 years or older (ref.)
|
4.00
|
|
|
|
|
Up to 23 years
|
3.00
|
0.906
|
0.651–1.25
|
0.072
|
|
24 to 29 years
|
4.00
|
1.263
|
1.007–1.59
|
|
|
30 to 39 years
|
3.00
|
1.083
|
0.859–1.37
|
|
|
Schooling
|
Up to high school (ref.)
|
2.00
|
|
|
|
|
University/college
|
2.00
|
1.23
|
0.897–1.74
|
< 0.001
|
|
Graduate school
|
5.00
|
2.08
|
1.538–2.88
|
|
|
Only two variables maintained statistical significance in the multiple logistic regression analysis: occupation and MHL score. Being a dentist and having a higher eHeals score were associated with a greater ability to identify false information related to dentistry (Table 6).
Table 6: Poisson linear regression.
|
Variables
|
Category
|
PR
|
CI (95%)
|
p-value*
|
|
Intercept
|
Total score
|
3.24
|
2.95–3.55
|
< 0.001
|
|
Occupation
|
Other fields (ref.)
|
|
|
|
|
Health field
|
1.52
|
1.25–1.85
|
< 0.001
|
|
Dentist
|
2.63
|
2.18–3.18
|
< 0.001
|
|
eHeals
|
Total score
|
1.01
|
1.00-1.03
|
0.009
|
|
Poisson regression adjusted for income (p < 0.001) and schooling (p < 0.001).
Legend: PR = prevalence ratio; CI = confidence interval; ref. = reference.
|
|
Discussion
Media health literacy (MHL) is a valuable tool for identifying fake health news available online. The literature suggests that higher levels of MHL are associated with a higher education, higher income, the female gender, a younger age, and working in the health field (Hanna et al., 2017; Valizadeh-Haghi and Rahmatizadeh, 2018; Gazibara et al., 2019; Kohan et al., 2019; Da Silva and Walmsley, 2019; Kristjánsdóttir et al., 2022; Lotto et al., 2023). However, few studies have investigated MHL in the context of dentistry, making this a pioneering study in investigating the relationship between this type of literacy and the ability to discern fake news specific to the dental field.
In our study, significant associations were found between higher MHL levels and having a postgraduate degree, monthly income higher than R$8,000, and working in the health field. These findings are in agreement with data reported in the literature, which indicates that individuals more familiar with scientific content and reliable sources have better critical thinking skills (Gazibara et al., 2019; Hakeem et al., 2023).
The increase in the dissemination of fake news related to health, especially during the COVID-19 pandemic, highlighted the negative impact of the infodemic on public health, causing insecurity and adherence to harmful behaviors (Garcia and Duarte, 2020; Al-Amad and Amal, 2021; Montagni et al., 2021; Kyabaggu et al., 2022; Biradar et al., 2022; Moretti et al., 2023). This phenomenon exposed flaws in the population’s ability to discern false information, which underscores the importance of developing MHL as a coping strategy.
In the field of dentistry, our data reveal that even dentists have difficulty recognizing misinformation. For instance, the statement that “the consumption of coffee interferes with teeth whitening” was considered true by many participants, although studies refute this association (Câmara et al., 2020). Similarly, the belief that “tooth eruption causes high fever” was mistakenly accepted by many respondents, including dentists, contrary to scientific evidence (Moreira et al., 2024).
The present results underscore the importance of educational strategies for fostering MHL in different subpopulations. Initiatives such as digital health training programs, accessible content, and the promotion of critical thinking are cited as effective at reducing the spread of fake news (Sotoudehrad et al., 2020; Lotto et al., 2020; Kristjánsdóttir et al., 2022; Kim et al., 2023; Saleem and Jan, 2024). Studies in the fields of medicine and nursing report similar results, revealing the extent of the problem.
The present study has some limitations that should be considered. The use of snowball sampling may have generated a homogeneous sample and the application of the eHeals questionnaire via the internet based on self-assessments may have introduced subjective bias (Peduzzi et al., 1996; Norman and Skinner, 2006).
A
Future studies should use larger samples directed at dentists, given the prevalence of conceptual errors among these professionals. The in-person administration of data collection instruments can also improve the reliability of the data. Lastly, the development of specific tools for assessing fake news in dentistry can significantly contribute to more precise educational interventions in the field.
Conclusion
This study revealed that higher levels of media health literacy (MHL) are associated with greater discernment capacity when faced with fake news related to dentistry. Factors such as schooling, income, and experience in the healthcare field were determinants of higher scores on the eHEALS instrument, reflecting greater critical aptitude for assessing health information encountered on the internet. However, difficulty in identifying false information was observed even among healthcare providers, which underscores the need to incorporate the development of MHL into educational strategies and professional practices.
Given the growing impact of misinformation related to health, the findings of this study highlight the urgent need for public policies and educational initiatives that promote digital health literacy as an essential tool for promoting evidence-based oral health. Future studies with larger samples, specific instruments, and in-person application could contribute to a deeper understanding of the phenomenon and the development of effective interventions to combat fake news in dentistry.
Acknowledgments
The authors would like to thank all participants who contributed their time and insights to this study. We are also grateful to Universidade Estadual de Maringá for institutional support.
A
Author Contribution
BZMP conceptualized and designed the study, led the analysis, and drafted the manuscript. ACPLS contributed to data collection and interpretation. DFC participated in literature review, administration of the survey, and data organization. MCB revised and edited the final manuscript. All authors reviewed, revised, and approved the final version of the manuscript.
A
Data Availability
The datasets generated and/or analyzed during the present study are available from the corresponding author upon reasonable request.
References
A
1.Garcia LP, Duarte E. Infodemia: excesso de quantidade em detrimento da qualidade das informações sobre a COVID-19. Epidemiol Serv Saude. 2020;29(4):e2020186. 10.1590/S1679-49742020000400019.
2.Raman R, Nair VK, Nedungadi P, Sahu AK, Kowalski R, Ramanathan S, et al. Fake News research trends, linkages to generative artificial intelligence and sustainable development goals. Heliyon. 2024;10(3):e24727. 10.1016/j.heliyon.2024.e24727.
3.Smith B, Magnani JW. New technologies, new disparities: the intersection of electronic health and digital health literacy. Int J Cardiol. 2019;292:280–2.
A
4.Do BN, et al. Health literacy, eHealth literacy, adherence to infection prevention and control procedures, lifestyle changes, and suspected COVID-19 symptoms among health care workers during lockdown: Online survey. J Med Internet Res. 2020;22(11):e19694. 10.2196/19694.
A
5.Hanna K, Sambrook P, Armfield JM, Brennan DS. Internet use, online information seeking and knowledge among third molar patients attending public dental services. Aust Dent J. 2017;62(3):323–30.
A
6.Gazibara T, et al. eHealth and adolescents in Serbia: psychometric properties of eHeals questionnaire and contributing factors to better online health literacy. Health Promot Int. 2019;34(4):770–8.
A
7.Baek JJH, et al. Network analysis and psychometric properties of the Brazilian version of the eHealth Literacy Scale in a dental clinic setting. Int J Med Inf. 2021;152:104475. 10.1016/j.ijmedinf.2021.104475.
A
8.Hakeem FF, et al. The association between electronic health literacy and oral health outcomes among dental patients in Saudi Arabia: A cross-sectional study. Healthcare. 2023;11(12):1804.
A
9.Valizadeh-Haghi S, Rahmatizadeh S. eHealth literacy and general interest in using online health information: A survey among patients with dental diseases. Online J Public Health Inf. 2018;10(3):e219.
A
10.Lotto M, et al. Parental-oriented educational mobile messages to aid in the control of early childhood caries in low socioeconomic children: A randomized controlled trial. J Dent. 2020;101:103452. 10.1016/j.jdent.2020.103452.
A
11.Lotto M, et al. eHEALS as a predictive factor of digital health information seeking behavior among Brazilian undergraduate students. Health Promot Int. 2023;38(4):daad057. 10.1093/heapro/daad057.
A
12.Al-Amad SH, Amal H. Anxiety among dental professionals and its association with their dependency on social media for health information: insights from the COVID-19 pandemic. BMC Psychol. 2021;9(1):9.
A
13.Da Silva MA, Walmsley AD. Fake news and dental education. Br Dent J. 2019;226(6):397–9.
A
14.Collet G, Dedeoğlu S, Erdemir AD. The power of digital influence: Assessing the impact of dental influencers on Instagram on oral health behaviors and misinformation. Int J Paediatr Dent. 2024;34(1):14–22. 10.1111/ipd.13120.
A
15.Barbosa JKL, de Oliveira LP, de Barros Souza RC. Cross-cultural adaptation and evidence of the validity of the eHealth Literacy Scale for use in Brazil. Rev Enferm Referência. 2022;1e21067. 10.12707/RV21067.
A
16.Kohan S, et al. Designing and evaluating an empowering program for breastfeeding: A mixed-methods study. Arch Iran Med. 2019;22(8):443–52.
A
17.Kristjánsdóttir Ó, et al. eHealth literacy and socioeconomic and demographic characteristics of parents of children needing paediatric surgery in Sweden. Nurs Open. 2022;10(2):509–24.
18.Montagni I, et al. Acceptance of a COVID-19 vaccine is associated with ability to detect fake news and health literacy. J Public Health. 2021;43(4):695–702.
A
19.Kyabaggu R, Marshall D, Ebuwei P, Ikenyei U. Health literacy, equity, and communication in the COVID-19 era of misinformation: Emergence of health information professionals in infodemic management. JMIR Infodemiology. 2022;2(1):e35014. 10.2196/35014.
A
20.Biradar S, Saumya S, Chauhan A. Combating the infodemic: COVID-19 induced fake news recognition in social media networks. Complex Intell Syst. 2023;9(3):2879–91.
A
21.Moretti V, et al. A web tool to help counter the spread of misinformation and fake news: Pre-post study among medical students to increase digital health literacy. JMIR Med Educ. 2023;18(9):e38377.
A
22.Câmara AC, Rezende TMB, Oliveira GC, Silva DMR. Effect of coffee and red wine on dental bleaching: A randomized clinical trial. Braz Oral Res. 2024;38:e094. 10.1590/1807-3107bor-2024.vol38.0094.
23.Moreira AL, da Silva CG, Franco MGC, Cunha RF. Association between eruption of primary teeth and systemic symptoms: A cross-sectional study. BMC Oral Health. 2024;24(1):106. 10.1186/s12903-024-03628-4.
24.Sotoudehrad F, et al. Investigating the relationship between media literacy and health literacy in Iranian adolescents, Isfahan, Iran. Int J Pediatr. 2020;8(5):11321.
A
25.Kim K, Shin S, Kim S, Lee E. The relation between eHealth literacy and health-related behaviors: Systematic review and meta-analysis. J Med Internet Res. 2023;30(25):e40778.
A
26.Saleem SM, Jan SS. Navigating the infodemic: Strategies and policies for promoting health literacy and effective communication. Front Public Health. 2023;12:11.
A
27.Peduzzi P, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9.
A
28.Norman CD, Skinner HA, eHEALS. The eHealth literacy scale. J Med Internet Res. 2006;8(4):e27. 10.2196/jmir.8.4.e27.