skyglide Explore Beyond
Mari, Jd. J. & Oquendo, M. A. Mental health consequences of COVID-19: the next global pandemic. Trend. Psychiatry Psychother. 42, 219–220 (2020).
Nochaiwong, S. et al. Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis. Sci. Rep. 11, 10173 (2021).
Chen, S. X. et al. Dual impacts of coronavirus anxiety on mental health in 35 societies. Sci. Rep. 11, 8925 (2021).
Taylor, S. & Asmundson, G. J. Life in a post-pandemic world: What to expect of anxiety-related conditions and their treatment. J. Anxiety Disord. 72, 102231 (2020).
Solmi, M. et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol. Psychiatry. http://www.nature.com/articles/s41380-021-01161-7 (2021).
Czeisler, M. É., Howard, M. E. & Rajaratnam, S. M. W. Mental Health During the COVID-19 Pandemic: Challenges, Populations at Risk, Implications, and Opportunities. Am. J. Health Promot. 35, 301–311 (2021).
Buckley, R. & Brough, P. Mental health: set up long-term cohort studies. Nature 595, 352–352 (2021).
Ellwardt, L. & Präg, P. Heterogeneous mental health development during the COVID-19 pandemic in the United Kingdom. Sci. Rep. 11, 15958 (2021).
Beutel, M. E. et al. Mental health and loneliness in the German general population during the COVID-19 pandemic compared to a representative pre-pandemic assessment. Sci. Rep. 11, 14946 (2021).
Ochnik, D. et al. Mental health prevalence and predictors among university students in nine countries during the COVID-19 pandemic: a cross-national study. Sci. Rep. 11, 18644 (2021).
Hampshire, A. et al. Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom. Nat. Commun. 12, 4111 (2021).
Skaik, R. & Inkpen, D. Using Social Media for Mental Health Surveillance. ACM Comput. Surv. 53, 1–31 (2021).
Rahman, R. A., Omar, K., Mohd Noah, S. A., Danuri, M. S. N. M. & Al-Garadi, M. A. Application of Machine Learning Methods in Mental Health Detection: A Systematic Review. IEEE Access 8, 183952–183964 (2020).
Chen, C., Ma, J., Susilo, Y., Liu, Y. & Wang, M. The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp. Res. Part C Emerg. Technol. 68, 285–299 (2016).
Wojcik, S. P., Hovasapian, A., Graham, J., Motyl, M. & Ditto, P. H. Conservatives report, but liberals display, greater happiness. Science 347, 1243–1246 (2015).
Le Glaz, A. et al. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. J. Med. Internet Res. 23, e15708 (2021).
Tewari, A., Chhabria, A., Khalsa, A. S., Chaudhary, S. & Kanal, H. A Survey of Mental Health Chatbots using NLP. SSRN Electron. J. https://www.ssrn.com/abstract=3833914 (2021).
Dean, H. J. & Boyd, R. L. Deep into that darkness peering: A computational analysis of the role of depression in Edgar Allan Poe’s life and death. J. Affect. Disord. 266, 482–491 (2020).
Calvo, R., Milne, D., Hussain, M. & Christensen, H. Natural language processing in mental health applications using non-clinical texts. Nat. Lang. Eng. 23, 649–685 (2017).
Ashokkumar, A. & Pennebaker, J. W. Social media conversations reveal large psychological shifts caused by COVID-19’s onset across U.S. cities. Sci. Adv. 7, https://doi.org/10.1126/sciadv.abg7843 (2021).
Vine, V., Boyd, R. L. & Pennebaker, J. W. Natural emotion vocabularies as windows on distress and well-being. Nat. Commun. 11, 4525 (2020).
Kim, J., Lee, J., Park, E. & Han, J. A deep learning model for detecting mental illness from user content on social media. Sci. Rep. 10, 11846 (2020).
Cook, H. V. & Jensen, L. J. A Guide to Dictionary-Based Text Mining. In R., L. & T, O. (eds.) Bioinformatics and Drug Discovery. Methods in Molecular Biology, 73–89 (Humana Press, New York, 2019), vol 1939 edn. http://link.springer.com/10.1007/978-1-4939-9089-4_5.
Franklin, E. Some theoretical considerations in off-the-shelf text analysis software. In Proceedings of the Student Research Workshop, 8–15 (INCOMA Ltd. Shoumen, BULGARIA, Hissar, Bulgaria, 2015). https://aclanthology.org/R15-2002.
Kennedy, B., Ashokkumar, A., Boyd, R. L. & Dehghani, M. Text Analysis for Psychology: Methods, Principles, and Practices https://psyarxiv.com/h2b8t/ (2021).
Wittgenstein, L. Philosophical Investigations (Wiley, Hoboken, NJ, 1953).
Su, C., Xu, Z., Pathak, J. & Wang, F. Deep learning in mental health outcome research: a scoping review. Transl. Psychiatry 10, 116 (2020).
Gkotsis, G. et al. Characterisation of mental health conditions in social media using Informed Deep Learning. Scie. Rep. 7, 45141 (2017).
Roy, A. et al. A machine learning approach predicts future risk to suicidal ideation from social media data. npj Digit. Med. 3, 78 (2020).
Shamshirband, S., Fathi, M., Dehzangi, A., Chronopoulos, A. T. & Alinejad-Rokny, H. A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues. J. Biomed. Inform. 113, 103627 (2021).
Chancellor, S. & De Choudhury, M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digit. Med. 3, 43 (2020).
Bloch, S. & Leydon, G. Conversation Analysis and Telephone Helplines for Health and Illness: A Narrative Review. Res. Lang. Soc. Interact. 52, 193–211 (2019).
IFOTES, Facts & Figures (2021). Retrieved 26 September 2023, https://www.ifotes.org/en/about.
Stufano, A. et al. Impact of COVID-19 emergency on the psychological well-being of susceptible individuals. Sci. Rep. 12, 11152 (2022).
Sampson, L. et al. Stressful life events and trajectories of depression symptoms in a U.S. military cohort. Sci. Rep. 12, 11026 (2022).
Hertz-Palmor, N. et al. Association among income loss, financial strain and depressive symptoms during COVID-19: Evidence from two longitudinal studies. J. Affect. Disord. 291, 1–8 (2021).
Ohrnberger, J., Anselmi, L., Fichera, E. & Sutton, M. The effect of cash transfers on mental health: Opening the black box – A study from South Africa. Soc. Sci. Med. 260, 113181 (2020).
de Lima, C. V. C. et al. Effects of quarantine on mental health of populations affected by Covid-19. J. Affect. Disord. 275, 253–254 (2020).
Wang, J. et al. Impact of psychosocial stressors on emotional and behavioral problems in Chinese adolescents during the COVID-19 period: the explanatory value of loneliness. Transl. Pediatr. 10, 2929–2940 (2021).
Evans, C. & Lewis, J.Analysing Semi-Structured Interviews Using Thematic Analysis: Exploring Voluntary Civic Participation Among Adults (SAGE Publications, Ltd., 1 Oliver’s Yard, 55 City Road London EC1Y 1SP United Kingdom, 2018). http://methods.sagepub.com/dataset/interviews-thematic-civic-participation.
Farrugia, B. Wasp (write a scientific paper): Sampling in qualitative research. Early Hum. Dev. 133, 69–71 (2019).
Terry, G., Hayfield, N., Clarke, V. & Braun, V. Thematic analysis. In The SAGE Handbook of Qualitative Research in Psychology, 17–36 (SAGE Publications Ltd, 1 Oliver’s Yard, 55 City Road London EC1Y 1SP, 2017). http://methods.sagepub.com/book/the-sage-handbook-of-qualitative-research-in-psychology-second-edition/i425.xml.
Flick, U. Triangulation. In Handbuch Qualitative Forschung in der Psychologie, 185–199 (Springer Fachmedien Wiesbaden, Wiesbaden, 2020). http://link.springer.com/10.1007/978-3-658-26887-9_23.
Saunders, B. et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual. Quant. 52, 1893–1907 (2018).
Hennink, M. M., Kaiser, B. N. & Marconi, V. C. Code Saturation Versus Meaning Saturation. Qual. Health Res. 27, 591–608 (2017).
Herzog, C., Handke, C. & Hitters, E. Analyzing Talk and Text II: Thematic Analysis. In The Palgrave Handbook of Methods for Media Policy Research, 385–401 (Springer International Publishing, Cham, 2019). http://link.springer.com/10.1007/978-3-030-16065-4_22.
Pfund, G. N., Hill, P. L. & Harriger, J. Video chatting and appearance satisfaction during COVID-19: Appearance comparisons and self-objectification as moderators. Int. J. Eating Disord.53, 2038–2043 (2020).
Pikoos, T. D., Buzwell, S., Sharp, G. & Rossell, S. L. The Zoom Effect: Exploring the Impact of Video Calling on Appearance Dissatisfaction and Interest in Aesthetic Treatment During the COVID-19 Pandemic. Aesthet. Surg. J. 41, NP2066–NP2075 (2021).
Lessard, L. M. & Puhl, R. M. Adolescents’ Exposure to and Experiences of Weight Stigma During the COVID-19 Pandemic. J. Pediatr. Psychol. 46, 950–959 (2021).
Rodgers, R. F. et al. The impact of the COVID-19 pandemic on eating disorder risk and symptoms. Int. J. Eat. Disord. 53, 1166–1170 (2020).
Movimiento de Integración y Liberación Homosexual XIX Informe anual de derechos humanos de la diversidad sexual y de género en chile (Hechos 2020) (2020).
Mac-Ginty, S., Jiménez-Molina, Á. & Martínez, V. Impacto de la pandemia por covid-19 en la salud mental de estudiantes universitarios en chile. Revista Chilena de Psiquiatría y Neurología de la Infancia y de la Adolescencia 32, 23–37 (2021).
Feeney, J. A. & Fitzgerald, J. Autonomy-connection tensions, stress, and attachment: The case of COVID-19. Curr. Opin. Psychol. 43, 18–23 (2022).
Clark, A. E., Nong, H., Zhu, H. & Zhu, R. Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic. China Econ. Rev. 68, 101629 (2021).
Engelhardt, B., Johnson, M. & Meder, M. E. Learning in the time of Covid-19: Some preliminary findings. Int. Rev. Econ. Educ. 37, 100215 (2021).
Gonzalez, T. et al. Influence of COVID-19 confinement on students’ performance in higher education. PLOS ONE 15, e0239490 (2020).
Aucejo, E. M., French, J., Ugalde Araya, M. P. & Zafar, B. The impact of COVID-19 on student experiences and expectations: Evidence from a survey. J. Public Econ. 191, 104271 (2020).
Mazza, M., Marano, G., Lai, C., Janiri, L. & Sani, G. Danger in danger: Interpersonal violence during COVID-19 quarantine. Psychiatr. Res. 289, 113046 (2020).
Neil, J. Domestic violence and COVID-19: Our hidden epidemic. Aust. J. Gen. Pract. 49 https://www1.racgp.org.au/ajgp/coronavirus/domestic-violence-and-covid-19 (2020).
Usher, K. et al. COVID-19 and family violence: Is this a perfect storm? Int. J. Mental Health Nurs. 30, 1022–1032 (2021).
Oh, H. Y., Marinovich, C., Jay, S., Zhou, S. & Kim, J. H. Abuse and suicide risk among college students in the united states: Findings from the 2019 healthy minds study. J. Affect. Disord. 282, 554–560 (2021).
Jankowiak, B. et al. Will i like myself if you hurt me? experiences of violence and adolescents’ self-esteem. Sustainability 13, 11620 (2021).
Kaplan, S. J., Pelcovitz, D., Salzinger, S., Mandel, F. & Weiner, M. Adolescent physical abuse and suicide attempts. J. Am. Acad. Child Adolesc. Psychiatry 36, 799–808 (1997).
Benitez, C., Southward, M. W., Altenburger, E. M., Howard, K. P. & Cheavens, J. S. The within-person effects of validation and invalidation on in-session changes in affect. Personal. Disord. 10, 406–415 (2019).
Newman, M. G., Llera, S. J., Erickson, T. M., Przeworski, A. & Castonguay, L. G. Worry and Generalized Anxiety Disorder: A Review and Theoretical Synthesis of Evidence on Nature, Etiology, Mechanisms, and Treatment. Annu. Rev. Clin. Psychol. 9, 275–297 (2013).
Marengo, D. & Montag, C. Digital Phenotyping of Big Five Personality via Facebook Data Mining: A Meta-Analysis. Digit. Psychol. 1, 52–64 (2020).
Azucar, D., Marengo, D. & Settanni, M. Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis. Personal. Individ. Differ. 124, 150–159 (2018).
