Engagement Analysis of Canadian Public Health and News Media Facebook Posts and Sentiment Analysis of Corresponding Comments during COVID-19
Abstract
:1. Introduction
- Classify the engagement rate of Facebook posts over time;
- Assess the proportion of negative sentiment on comments over time;
- Assess how trends in the proportion of trinary sentiment (positive, neutral, or negative emotional response) of comments may affect the total number of comments per post.
2. Materials and Methods
2.1. Data Collection
2.2. Data Analysis
2.3. Statistical Analysis
3. Results
3.1. Change in Post Engagement Rate over Time
3.2. Change in Comment Sentiment over Time
3.3. Relationship between Sentiment Scores and Total Number of Comments Per Post
4. Discussion
4.1. Monitor and Increase Social Media Engagement
4.2. Monitor the Sentiment of Comments on Social Media to Correct for Exemplars
4.3. Pay Close Attention to Sentiment When Key Crisis Events Occur
5. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Banks, K. In the Midst of the Pandemic, Academics Are Fighting a Rising ‘Infodemic’. University Affairs. 2020. Available online: https://www.universityaffairs.ca/features/feature-article/in-the-midst-of-the-pandemic-academics-are-fighting-a-rising-infodemic/ (accessed on 5 August 2021).
- Esri Canada. Canadian Outbreak At-A-Glance. 2020. Available online: https://resources-covid19canada.hub.arcgis.com (accessed on 7 December 2020).
- National Collaborating Centre for Methods and Tools. Rapid Review Update 1: What Are Best Practices for Risk Communication and Strategies to Mitigate Risk Behaviours? National Collaborating Centre for Methods and Tools. Available online: https://www.nccmt.ca/uploads/media/media/0001/02/5f7d164da82e9565106ae14b871bbe89b45606ad.pdf (accessed on 24 March 2021).
- Government of Canada. Coronavirus Disease (COVID-19): Prevention and Risks. 2020. Available online: https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection/prevention-risks.html (accessed on 13 November 2020).
- World Health Organization. Coronavirus Disease (COVID-19) Advice for the Public. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public (accessed on 5 January 2021).
- Ghio, D.; Lawes-Wickwar, S.; Tang, M.Y.; Epton, T.; Howlett, N.; Jenkinson, E.; Stanescu, S.; Westbrook, J.; Kassianos, A.P.; Watson, D.; et al. What Influences People’s Responses to Public Health Messages for Managing Risks and Preventing Infectious Diseases? A Rapid Systematic Review of the Evidence and Recommendations. PsyArXiv 2020. Available online: https://psyarxiv.com/nz7tr/ (accessed on 27 October 2020).
- MacKay, M.; Colangeli, T.; Thaivalappil, A.; Del Bianco, A.; McWhirter, J.; Papadopoulos, A. A Review and Analysis of the Literature on Public Health Emergency Communication Practices. J. Community Health 2021. Available online: https://link.springer.com/10.1007/s10900-021-01032-w (accessed on 25 October 2021).
- CDC. CERC Manual|Crisis & Emergency Risk Communication (CERC). 2018. Available online: https://emergency.cdc.gov/cerc/manual/index.asp (accessed on 12 August 2021).
- Henry, B. Canadian Pandemic Influenza Preparedness: Communications strategy. Can. Commun. Dis. Rep. 2018, 44, 106–109. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449096/ (accessed on 4 October 2020). [CrossRef]
- Quinn, P. Crisis Communication in Public Health Emergencies: The Limits of ‘Legal Control’ and the Risks for Harmful Outcomes in a Digital Age. Life Sci. Soc. Policy 2018, 14, 4. Available online: https://lsspjournal.biomedcentral.com/articles/10.1186/s40504-018-0067-0 (accessed on 9 June 2021). [CrossRef] [PubMed] [Green Version]
- Seeger, M.W. Best Practices in Crisis Communication: An Expert Panel Process. J. Appl. Commun. Res. 2006, 34, 232–244. Available online: http://www.tandfonline.com/doi/abs/10.1080/00909880600769944 (accessed on 14 June 2021). [CrossRef]
- Vaughan, E.; Tinker, T. Effective Health Risk Communication about Pandemic Influenza for Vulnerable Populations. Am. J. Public Health 2009, 99, S324–S332. Available online: http://ajph.aphapublications.org/doi/10.2105/AJPH.2009.162537 (accessed on 14 June 2021). [CrossRef] [PubMed]
- Seeger, M.; Pechta, L.; Price, S.; Lubell, K.M.; Rose, D.; Sapru, S.; Chansky, M.C.; Smith, B.J. A Conceptual Model for Evaluating Emergency Risk Communication in Public Health. Health Secur. 2018, 16, 193–203. Available online: https://www-liebertpub-com.subzero.lib.uoguelph.ca/doi/abs/10.1089/hs.2018.0020 (accessed on 5 August 2021).
- Veil, S.R.; Buehner, T.; Palenchar, M.J. A Work-In-Process Literature Review: Incorporating Social Media in Risk and Crisis Communication. J. Contingencies Crisis Manag. 2011, 19, 110–122. Available online: http://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-5973.2011.00639.x (accessed on 2 February 2021). [CrossRef]
- Wendling, C.; Radisch, J.; Jacobzone, S. The Use of Social Media in Risk and Crisis Communication. 2013. Available online: https://www.oecd-ilibrary.org/governance/the-use-of-social-media-in-risk-and-crisis-communication_5k3v01fskp9s-en (accessed on 11 August 2021).
- Chan, M.S.; Winneg, K.; Hawkins, L.; Farhadloo, M.; Jamieson, K.H.; Albarracín, D. Legacy and social media respectively influence risk perceptions and protective behaviors during emerging health threats: A multi-wave analysis of communications on Zika virus cases. Soc. Sci. Med. 2018, 212, 50–59. Available online: https://linkinghub.elsevier.com/retrieve/pii/S0277953618303630 (accessed on 25 October 2021). [CrossRef] [PubMed]
- Choi, D.-H.; Yoo, W.; Noh, G.-Y.; Park, K. The impact of social media on risk perceptions during the MERS outbreak in South Korea. Comput. Hum. Behav. 2017, 72, 422–431. Available online: https://linkinghub.elsevier.com/retrieve/pii/S074756321730153X (accessed on 25 October 2021). [CrossRef]
- Hassan, M.S.; Halbusi, H.A.; Najem, A.; Razali, A.; Williams, K.A.; Mustamil, N.M. Impact of Risk Perception on Trust in Government and Self-Efficiency During COVID-19 pandemic: Does Social Media Content Help Users Adopt Preventative Measures? Review. 2020. Available online: https://www.researchsquare.com/article/rs-43836/v1 (accessed on 25 October 2021).
- Oh, S.-H.; Lee, S.Y.; Han, C. The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception. Health Commun. 2020, 36, 972–981. [Google Scholar] [CrossRef] [PubMed]
- Alamoodi, A.; Zaidan, B.; Zaidan, A.; Albahri, O.; Mohammed, K.; Malik, R.; Almahdi, E.; Chyad, M.; Tareq, Z.; Hameed, H.; et al. Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert Syst. Appl. 2021, 167, 114155. Available online: https://www.sciencedirect.com/science/article/pii/S0957417420308988 (accessed on 5 August 2021). [CrossRef]
- Jiang, H.; Luo, Y.; Kulemeka, O. Social media engagement as an evaluation barometer: Insights from communication executives. Public Relat. Rev. 2016, 42, 679–691. Available online: https://linkinghub.elsevier.com/retrieve/pii/S0363811115300461 (accessed on 25 October 2021). [CrossRef]
- PR Newswire. Facebook Reports First Quarter 2020 Results. CISION PR Newswire. 2020. Available online: https://www.prnewswire.com/news-releases/facebook-reports-first-quarter-2020-results-301049682.html (accessed on 5 August 2021).
- Coombs, T.; Holladay, S. How publics react to crisis communication efforts: Comparing crisis response reactions across sub-arenas. J. Commun. Manag. 2014, 18, 40–57. Available online: https://www.researchgate.net/publication/263270956_How_publics_react_to_crisis_communication_efforts_Comparing_crisis_response_reactions_across_sub-arenas (accessed on 5 August 2021). [CrossRef]
- Chen, Q.; Min, C.; Zhang, W.; Wang, G.; Ma, X.; Evans, R. Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis. Comput. Hum. Behav. 2020, 110, 106380. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151317/ (accessed on 30 December 2021). [CrossRef] [PubMed]
- Pang, P.C.-I.; Cai, Q.; Jiang, W.; Chan, K.S. Engagement of Government Social Media on Facebook during the COVID-19 Pandemic in Macao. Int. J. Environ. Res. Public Health 2021, 18, 3508. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036686/ (accessed on 30 December 2021). [CrossRef] [PubMed]
- MonkeyLearn. Sentiment Analysis: The Go-To Guide. MonkeyLearn, 2021. Available online: https://monkeylearn.com/sentiment-analysis/ (accessed on 5 August 2021).
- Ji, Y.G.; Chen, Z.F.; Tao, W.; Cathy Li, Z. Functional and emotional traits of corporate social media message strategies: Behavioral insights from S&P 500 Facebook data. Public Relat. Rev. 2019, 45, 88–103. Available online: https://linkinghub.elsevier.com/retrieve/pii/S0363811118303680 (accessed on 30 December 2021).
- Kumar, A.; Khan, S.U.; Kalra, A. COVID-19 pandemic: A sentiment analysis. Eur. Heart J. 2020, 41, 3782–3783. Available online: https://academic.oup.com/eurheartj/article/41/39/3782/5873149 (accessed on 30 December 2021). [CrossRef]
- de las Heras-Pedrosa, C.; Sánchez-Núñez, P.; Peláez, J.I. Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems. Int. J. Environ. Res. Public Health 2020, 17, 5542. [Google Scholar] [CrossRef] [PubMed]
- Manguri, K.H.; Ramadhan, R.N.; Mohammed Amin, P.R. Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks. Kurd. J. Appl. Res. 2020, 5, 54–65. [Google Scholar] [CrossRef]
- Ji, X.; Chun, S.A.; Wei, Z.; Geller, J. Twitter sentiment classification for measuring public health concerns. Soc. Netw. Anal. Min. 2015, 5, 1–25. [Google Scholar] [CrossRef]
- Shen, Y. COVID-19 Outbreak: Tweet Analysis on Face Masks. Towards Data Science. 2020. Available online: https://towardsdatascience.com/covid-19-outbreak-tweet-analysis-on-face-masks-27ef5db199dd (accessed on 15 October 2021).
- Winter, S.; Brückner, C.; Krämer, N.C. They Came, They Liked, They Commented: Social Influence on Facebook News Channels. Cyberpsychology Behav. Soc. Netw. 2015, 18, 431–436. Available online: https://liebertpub.com/doi/10.1089/cyber.2015.0005 (accessed on 5 August 2021). [CrossRef] [PubMed]
- Grady, D.A.; Hollifield, A.; Sturgill, A. (Eds.) The Golden Age of Data: Media Analytics in Study & Practice, 1st ed.; Routledge: New York, NY, USA, 2019; Available online: https://www.taylorfrancis.com/books/9781000713909 (accessed on 6 August 2021).
- Peters, R.G.; Covello, V.T.; McCallum, D.B. The Determinants of Trust and Credibility in Environmental Risk Communication: An Empirical Study. Risk Anal. 1997, 17, 43–54. Available online: http://onlinelibrary.wiley.com/doi/abs/10.1111/j.1539-6924.1997.tb00842.x (accessed on 6 October 2020). [CrossRef]
- Lee, E.-J.; Jang, Y.J. What Do Others’ Reactions to News on Internet Portal Sites Tell Us? Effects of Presentation Format and Readers’ Need for Cognition on Reality Perception. Commun. Res. 2010, 37, 825–846. [Google Scholar] [CrossRef]
- Peter, C.; Rossmann, C.; Keyling, T. Exemplification 2.0: Roles of direct and indirect social information in conveying health messages through social network sites. J. Media Psychol. Theor. Methods Appl. 2014, 26, 19. Available online: https://psycnet-apa-org.subzero.lib.uoguelph.ca/fulltext/2014-10231-004.pdf (accessed on 6 August 2021). [CrossRef]
- Wagner, D. Managing Negative Comments Posted on Social Media. PhD Thesis, Walden University, Minneapolis, Minnesota, 2003. Available online: https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=2555&context=dissertations (accessed on 6 August 2021).
- Anthony, K. For Canadians, Trust in News Media Has Fallen: Study. 2019. Available online: https://mediaincanada.com/2019/09/13/for-canadians-trust-in-news-media-has-fallen-study/ (accessed on 5 August 2021).
- Canada Guide. News and Media. The Canada Guide. 2021. Available online: https://thecanadaguide.com/basics/news-and-media/ (accessed on 30 December 2021).
- Canadian Communications Foundation. CTV Television Network|History of Canadian Broadcasting. History of Canadian Broadcasting. 2021. Available online: https://www.broadcasting-history.ca/listing_and_histories/ctv-television-network (accessed on 30 December 2021).
- Microsoft Corporation. Microsoft Excel. 2018. Available online: https://office.microsoft.com/excel (accessed on 5 August 2021).
- Calderon, N.A.; Fisher, B.; Hemsley, J.; Ceskavich, B.; Jansen, G.; Marciano, R.; Lemieux, V.L. Mixed-initiative social media analytics at the World Bank: Observations of citizen sentiment in Twitter data to explore “trust” of political actors and state institutions and its relationship to social protest. In Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 29 October–1 November 2015; pp. 1678–1687. [Google Scholar]
- Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A. Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 2010, 61, 2544–2558. Available online: https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.21416 (accessed on 14 June 2021). [CrossRef] [Green Version]
- SentiStrength. SentiStrength Results. Available online: http://sentistrength.wlv.ac.uk/results.php?text=FLUS+MUTATE+year+after+year+that%27s+why+they+can+never+get+the+flu+shot+right+and+yet+we%27re+supposed+&submit=Detect+Sentiment (accessed on 1 August 2021).
- Thelwall, M. Heart and Soul: Sentiment Strength Detection in the Social Web with. 2013. Available online: https://www.semanticscholar.org/paper/Heart-and-Soul-%3A-Sentiment-Strength-Detection-in-Thelwall/2d5c5bce531b454a9a79eaabcb835be5fd977ea1 (accessed on 5 August 2021).
- Socialbakers. Formulas Revealed: The Facebook and Twitter Engagement Rate. Available online: https://www.socialbakers.com/blog/467-formulas-revealed-the-facebook-and-twitter-engagement-rate (accessed on 5 August 2021).
- MacKay, M.; Colangeli, T.; Gillis, D.; McWhirter, J.; Papadopoulos, A. Examining Social Media Crisis Communication during Early COVID-19 from Public Health and News Media for Quality, Content, and Corresponding Public Sentiment. Int. J. Environ. Res. Public Health 2021, 18, 7986. Available online: https://www.mdpi.com/1660-4601/18/15/7986 (accessed on 6 August 2021). [CrossRef] [PubMed]
- Jipa. 2021 Social Media Industry Benchmarks|Socialinsider. Socialinsider Blog: Social Media Marketing Insights and Industry Tips. 2021. Available online: https://www.socialinsider.io/blog/social-media-industry-benchmarks/ (accessed on 5 August 2021).
- Henrich, N.; Holmes, B. Communicating During a Pandemic: Information the Public Wants About the Disease and New Vaccines and Drugs. Health Promot. Pract. 2011, 12, 610–619. Available online: http://journals.sagepub.com/doi/10.1177/1524839910363536 (accessed on 18 August 2021). [CrossRef] [PubMed]
- Aylesworth-Spink, S. Falling in Line: News Media and Public Health Response during the 2009 H1N1 Outbreak in Canada. Ph.D. Thesis, Queen’s University, Kingston, Canada, 2015. Available online: https://qspace.library.queensu.ca/handle/1974/12948?show=full (accessed on 5 August 2021).
- Gray, L.; MacDonald, C.; Mackie, B.; Paton, D.; Johnston, D.; Baker, M.G. Community responses to communication campaigns for influenza A (H1N1): A focus group study. BMC Public Health 2012, 12, 205. Available online: http://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-12-205 (accessed on 21 October 2020). [CrossRef] [PubMed] [Green Version]
- Cloes, R.; Ahmad, A.; Reintjes, R. Risk Communication During the 2009 Influenza A (H1N1) Pandemic: Stakeholder Experiences from Eight European Countries. Disaster Med. Public Health Prep. 2015, 9, 127–133. Available online: https://www.cambridge.org/core/product/identifier/S1935789314001244/type/journal_article (accessed on 25 October 2021). [CrossRef] [PubMed]
- Kok, G.; Jonkers, R.; Gelissen, R.; Meertens, R.; Schaalma, H.; de Zwart, O. Behavioural intentions in response to an influenza pandemic. BMC Public Health 2010, 10, 174. Available online: https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-10-174 (accessed on 25 October 2021). [CrossRef]
- Gesser-Edelsburg, A.; Mordini, E.; James, J.J.; Greco, D.; Green, M.S. Risk Communication Recommendations and Implementation During Emerging Infectious Diseases: A Case Study of the 2009 H1N1 Influenza Pandemic. Disaster Med. Public Health Prep. 2014, 8, 158–169. Available online: https://www.cambridge.org/core/product/identifier/S1935789314000275/type/journal_article (accessed on 21 October 2020). [CrossRef] [Green Version]
- King, C.L.; Chow, M.Y.K.; Wiley, K.E.; Leask, J. Much ado about flu: A mixed methods study of parental perceptions, trust and information seeking in a pandemic. Influenza Other Respir. Viruses 2018, 12, 514–521. Available online: http://doi.wiley.com/10.1111/irv.12547 (accessed on 21 October 2020). [CrossRef] [PubMed] [Green Version]
- Lyu, S.-Y.; Chen, R.-Y.; Wang, S.S.; Weng, Y.-L.; Peng, E.Y.-C.; Lee, M.-B. Perception of spokespersons’ performance and characteristics in crisis communication: Experience of the 2003 severe acute respiratory syndrome outbreak in Taiwan. J. Formos. Med. Assoc. 2013, 112, 600–607. Available online: https://linkinghub.elsevier.com/retrieve/pii/S0929664612005876 (accessed on 21 October 2020). [CrossRef]
- McFadden, S.M.; Malik, A.A.; Aguolu, O.G.; Willebrand, K.S.; Omer, S.B. Perceptions of the adult US population regarding the novel coronavirus outbreak. PLoS ONE 2020, 15, e0231808. Available online: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231808 (accessed on 16 December 2020). [CrossRef]
- Benham, J.L.; Atabati, O.; Oxoby, R.J.; Mourali, M.; Shaffer, B.; Sheikh, H.; Boucher, J.C.; Constantinescu, C.; Parsons Leigh, J.; Ivers, N.M.; et al. COVID-19 Vaccine–Related Attitudes and Beliefs in Canada: National Cross-sectional Survey and Cluster Analysis. JMIR Public Health Surveill. 2021, 7, e30424. Available online: https://publichealth.jmir.org/2021/12/e30424 (accessed on 30 December 2021). [CrossRef]
- Fung, I.C.-H.; Tse, Z.T.H.; Fu, K.-W. The use of social media in public health surveillance. West. Pac. Surveill. Response J. 2015, 6, 3–6. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542478/ (accessed on 30 December 2021). [CrossRef]
- Hootsuite. Social Media Marketing & Management Dashboard. Available online: https://www.hootsuite.com/ (accessed on 30 December 2021).
- Chen, J. Why Brands Need a Social Media Monitoring Strategy. Sprout Social. 2020. Available online: https://sproutsocial.com/insights/social-media-monitoring/ (accessed on 30 December 2021).
- Renyolds, B. Crisis and Emergency Risk Communication: Pandemic Influenza; US Department of Health and Human Services: Washington, DC, USA, 2007. Available online: https://emergency.cdc.gov/cerc/resources/pdf/cerc-pandemicflu-oct07.pdf (accessed on 6 August 2021).
- Cooper, P. How the Facebook Algorithm Works in 2021 and How to Work with It. Hootsuite. 2021. Available online: https://blog.hootsuite.com/facebook-algorithm/ (accessed on 6 August 2021).
- Centers for Disease Control and Prevention. CERC: Psychology of a Crisis; US Department of Health and Human Services: Washington, DC, USA, 2019; p. 16. Available online: https://emergency.cdc.gov/cerc/ppt/CERC_Psychology_of_a_Crisis.pdf (accessed on 6 August 2021).
- Statista. Canada Facebook Users by Age 2021. Available online: https://www.statista.com/statistics/863754/facebook-user-share-in-canada-by-age/ (accessed on 6 August 2021).
Source | Followers * (n) | Number of Posts (n) | Comments (n) | Reactions (n) | Shares (n) | Average Post Engagement Rate (%) |
---|---|---|---|---|---|---|
Healthy Canadians | 352,822 | 112 | 2211 | 65,111 | 42,229 | 0.3016 |
CBC News | 2,688,920 | 157 | 11,554 | 113,043 | 123,082 | 0.0707 |
CTV News | 977,636 | 169 | 13,009 | 99,152 | 147997 | 0.1787 |
Healthy Canadians | ||||
B | B (95% CI) | SE(B) | p Value | |
(Intercept) | 1.97 | (1.91, 2.03) | 0.03 | <0.001 |
Number of comments | −4.2 × 10−6 | (−1.7 × 10−4, 1.6 × 10−4) | 8.04 × 10−5 | 0.958 |
News media | ||||
B | B 95% (95% CI) | SE(B) | p Value | |
(Intercept) | 1.85 | (1.82, 1.87) | 0.014 | <0.001 |
Number of comments | 3.06 × 10−5 | (−2.35 × 10−5, 8.47 × 10−5) | 2.75 × 105 | 0.267 |
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MacKay, M.; Colangeli, T.; Gosselin, S.; Neumann, S.; Papadopoulos, A. Engagement Analysis of Canadian Public Health and News Media Facebook Posts and Sentiment Analysis of Corresponding Comments during COVID-19. Psych 2022, 4, 60-70. https://doi.org/10.3390/psych4010005
MacKay M, Colangeli T, Gosselin S, Neumann S, Papadopoulos A. Engagement Analysis of Canadian Public Health and News Media Facebook Posts and Sentiment Analysis of Corresponding Comments during COVID-19. Psych. 2022; 4(1):60-70. https://doi.org/10.3390/psych4010005
Chicago/Turabian StyleMacKay, Melissa, Taylor Colangeli, Sydney Gosselin, Sophie Neumann, and Andrew Papadopoulos. 2022. "Engagement Analysis of Canadian Public Health and News Media Facebook Posts and Sentiment Analysis of Corresponding Comments during COVID-19" Psych 4, no. 1: 60-70. https://doi.org/10.3390/psych4010005
APA StyleMacKay, M., Colangeli, T., Gosselin, S., Neumann, S., & Papadopoulos, A. (2022). Engagement Analysis of Canadian Public Health and News Media Facebook Posts and Sentiment Analysis of Corresponding Comments during COVID-19. Psych, 4(1), 60-70. https://doi.org/10.3390/psych4010005