The Association of Social Media Use and Psychiatric Diagnoses Among Missouri College Students
Notice bibliographique
Résumé
Introduction: “Many college students experience significant mental health difficulties, with the COVID-19 pandemic exacerbating these concerns (Sonet al., 2020). Mental health is significantly impacted by social media use, and this relationship has become increasingly complex after the COVID-19 pandemic (Haddad et al., 2021).” Research shows that depression, eating disorders, and anxiety are associated with more time spent on social media (Alam et al., 2021; Khan et al., 2019; Reighm et al., 2019; Sansha et al., 2017; Sidani et al., 2016). To follow-up on this research and assess the relationship between social media usage and psychiatric diagnoses among Missouri college students, we hypothesized that undergraduate students diagnosed with psychiatric disorders within the past year are spending more hours on social media per week. Methods: Cross-sectional data from 2022 were obtained from the Missouri College Health Behavior Survey (MACHB), a retrospective, self-report survey administered to undergraduate students at several universities in Missouri. Descriptive statistics were examined for social media use and the frequency of reported past-year diagnoses. A multiple regression model in SPSS was conducted to examine the effect of past-year diagnoses of anxiety, depression, PTSD, OCD/ADHD/ODD, sleep issues, and eating disorders on the average hours spent on social media per week. Results: The diagnosis of anxiety within the past year significantly predicted greater weekly use of social media (M = 19.30, SD = 17.90), compared to those without anxiety (M = 17.32, SD = 15.89), B = 2.06, p = .003. Students with PTSD spent significantly less time (M = 17.16, SD = 18.07) using social media than those not diagnosed (M = 18.09, SD = 16.54), B = -2.32, p = .029.nSimilarly, those with OCD/ADD/ADHD spent less time on social media (M = 17.28, SD = 16.68) than students not diagnosed (n = 3,264, M = 18.15, SD = 16.65), B = -1.68, p = .031. Past-year diagnoses of depression, eating disorders, and chronic sleep issues were not associated with social media use. Discussion: Students diagnosed with anxiety in the past year spend more time on social media than those who were not, consistent with previous research (Alam et al., 2021; Bettman et al., 2020; Maras et al., 2015; Riehm et al., 2019; Shensa et al., 2017; Vannucci et al., 2017). Contrary to hypotheses and prior research (Alam et al., 2021; Bettman et al., 2020; Khan et al., 2019; Maras et al., 2015; Riehm et al., 2019; Shensa et al., 2017), past-year OCD/ADD/ADHD and PTSD diagnoses were associated with less time spent on social media. However, viewing sensitive content on social media and experiencing cyber bullying has been found to elicit PTSD symptoms (McHugh et al. (2018), which can potentially help explain less time spent on social media among those diagnosed with PTSD. No associations were found between social media usage and eating disorders, major depressive disorder, or chronic sleep issues, despite some research highlighting associations with these disorders (Alam et al., 2021; Bettman et al., 2020; Khan et al., 2019; Maras et al., 2015; Riehm et al., 2019; Shensa et al., 2017; Sidani et al., 2016). Future research may include investigating the impact of social media usage on symptoms of anxiety disorders to determine directionality of the association. Additionally, Faulhaber et al. (2023) found that limiting social media use was associated with statistically significantly decreased anxiety compared to pretreatment. Further research testing various treatment approaches to decrease time spent on social media among individuals diagnosed with anxiety. References: Alam, M. K., Ali, F. B., Banik, R., Yasmin, S., & Salma, N. (2022). Assessing the mental health condition of home-confined university level students of Bangladesh due to the COVID-19 pandemic. Journal of Public Health, 30(7), 1685–1692. https://doi.org/10.1007/s10389-021-01542-w Bettmann, J.E., Anstadt, G., Casselman, B. et al. Young Adult Depression and Anxiety Linked to Social Media Use: Assessment and Treatment. Clin Soc Work J 49, 368–379 (2021). https://doi-org.ezproxy.umsl.edu/10.1007/s10615-020-00752-1. Faulhaber, M. E., Lee, J. E., & Gentile, D. A. (2023). The Effect of Self-Monitoring Limited Social Media Use on Psychological Well-Being. Technology, Mind, and Behavior, 4(2: Summer 2023). https://doi.org/10.1037/tmb0000111. Haddad, J. M., Macenski, C., Mosier-Mills, A., Hibara, A., Kester, K., Schneider, M.,... & Liu, C. H. (2021). The impact of social media on college mental health during the COVID-19 pandemic: a multinational review of the existing literature. Current psychiatry reports, 23, 1-12. Khan, A., Uddin, R., & Lee, E.-Y. (2021). Excessive recreational Internet use was associated with poor mental health in adolescents. Acta Paediatrica, 110(2), 571–573. https://doi.org/10.1111/apa.15528 Maras, D., Flament, M. F., Murray, M., Buchholz, A., Henderson, K. A., Obeid, N., & Goldfield, G. S. (2015). Screen time is associated with depression and anxiety in Canadian youth. Preventive medicine, 73, 133–138. https://doi-.org.ezproxy.umsl.edu/10.1016/j.ypmed.2015.01.029. McHugh, B.C., Wisniewski, P., Rosson, M.B. and Carroll, J.M. (2018), "When social media traumatizes teens: The roles of online risk exposure, coping, and post-traumatic stress", Internet Research, Vol. 28 No. 5, pp. 1169-1188. https://doi.org/10.1108/IntR-02-2017-0077. Riehm, K. E., Feder, K. A., Tormohlen, K. N., Crum, R. M., Young, A. S., Green, K. M., Pacek, L. R., La Flair, L. N., & Mojtabai, R. (2019). Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. JAMA Psychiatry, 76(12), 1266. https://doi.org/10.1001/jamapsychiatry.2019.2325 Shensa, A., Escobar-Viera, C. G., Sidani, J. E., Bowman, N. D., Marshal, M. P., & Primack, B. A. (2017). Problematic social media use and depressive symptoms among U.S. young adults: A nationally-representative study. Social Science & Medicine, 182, 150–157. https://doi.org/10.1016/j.socscimed.2017.03.061 Sidani, J. E., Shensa, A., Hoffman, B., Hanmer, J., & Primack, B. A. (2016). The Association between Social Media Use and Eating Concerns among U.S. Young Adults. Journal of the Academy of Nutrition and Dietetics, 116(9), 1465–1472. https://doi.org/10.1016/j.jand.2016.03.021 Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students' mental health in the United States:Interview survey study. Journal of medical internet research, 22(9), e21279. Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207, 163–166. https://doi.org/10.1016/j.jad.2016.08.040
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».