Screen Time and Adolescent Well-being: Synthesizing Evidence from Large-Scale National Surveys – Lumina Literati Publishing
Screen Time and Adolescent Well-being: Synthesizing Evidence from Large-Scale National Surveys book cover

Screen Time and Adolescent Well-being

Synthesizing Evidence from Large-Scale National Surveys

By

Published by Lumina Literati Publishing
Publication Year:
ISBN: 978-627-7813-54-3

Book Overview

Screen Time and Adolescent Well-being: Synthesizing Evidence from Large-Scale National Surveys begins from a single, clarifying premise: the question of whether screens are harming young people deserves better science than it has so far received. Every year, new headlines proclaim a crisis of adolescent mental health driven by smartphones and social media, yet the empirical record underlying these claims remains riddled with measurement error, confounding variables, and reverse-causal traps that conventional analyses can only partially address. This volume offers a rigorous, methodologically transparent synthesis of what large-scale national survey data actually reveal — and what they do not.

The book opens with a sustained examination of the methodological paradigms that have shaped the field, exposing how recall bias, social desirability distortion, and the consistent overestimation of self-reported screen time have compromised decades of survey evidence. It traces the transition from subjective measurement to passive telemetry and device-logged metrics, while interrogating what it means to operationalize “well-being” across cultures and instruments — from the PHQ-9 and RCADS to flourishing scales and relational metrics of peer attachment and family cohesion.

Moving from method to theory, the volume deconstructs the “dose-response” fallacy that has dominated public discourse, re-examining the displacement hypothesis and demonstrating why linear models of screen-time harm fail to capture the non-linear, context-dependent realities of adolescent digital engagement. Subsequent chapters address the sociological dimensions of stratification and the new digital divide, propose a nuanced typology that differentiates screen use by content, context, and connection, and close with evidence-based policy recommendations and future research directions.

The central argument is neither alarmist nor dismissive: forward causation from screen time to depression is consistently weak once properly disentangled from reverse causation, stable individual differences, and shared external shocks — and the field must move from blanket reduction mandates toward precision, developmentally attuned guidance.

About the Author

Dr. Seema Gul

Assistant Professor of Psychology, General Studies Department, College of Sciences and Humanities, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia · Email: sgul@psu.edu.sa

Dr. Seema Gul is an Assistant Professor of Psychology at Prince Sultan University, Riyadh, Kingdom of Saudi Arabia. She holds a Ph.D. in Psychology and possesses extensive experience in teaching, research, and academic leadership.

Her research interests include Psychological Testing, Clinical Psychology, Social Psychology, Educational Psychology, and Family Psychology, with a particular emphasis on adolescent family adjustment, mental health and well-being. Dr. Gul has made significant academic contributions through the presentation of research papers at national and international conferences and through publications in peer-reviewed international journals.

Prior to her appointment at Prince Sultan University, Dr. Gul served as Chair of the Department of Psychology at the International Islamic University, Islamabad, Pakistan, where she also held the administrative role of Provost of the Girls’ Hostel. Her academic and administrative career reflects a sustained commitment to higher education, research excellence, and institutional development.

Endorsements & Reviews

“A masterclass in methodological honesty. Dr. Gul dismantles the moral panic surrounding adolescent screen time with the precision of a careful scientist and the clarity of a gifted communicator. This is the synthesis the field has been waiting for.”

“By exposing the recall biases, reverse-causal traps, and dose-response fallacies that have distorted public discourse, this volume sets a new standard for how population-level evidence on digital media and youth well-being should be evaluated. Essential reading for researchers, policymakers, and educators alike.”

“Dr. Gul’s nuanced typology of content, context, and connection offers a genuinely useful alternative to the blunt instrument of total screen time. The closing chapters on evidence-based policy are among the most responsible and actionable I have encountered in this literature.”

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Screen Time and Adolescent Well-being: Synthesizing Evidence from Large-Scale National Surveys

References

Showing 10 of 292 referencesShowing all 292 references

1
Andriyani, F., Cocker, K., Priambadha, A., & Biddle, S. (2023). Physical activity and sedentary behaviour of male adolescents in Indonesia during the COVID-19 pandemic: a mixed-method case study using accelerometers, automated wearable cameras, diaries, and interviews. Journal of Activity Sedentary and Sleep Behaviors, 2(1). https://doi.org/10.1186/s44167-022-00014-0
2
Browne, D., May, S., Pietra, P., Christakis, D., Asamoah, T., Hale, L., … & Neville, R. (2019). From ‘screen time’ to the digital level of analysis: protocol for a scoping review of digital media use in children and adolescents. BMJ Open, 9(11), e032184. https://doi.org/10.1136/bmjopen-2019-032184
3
Demers‐Potvin, É., White, M., Kent, M., Nieto, C., White, C., Zheng, X., … & Vanderlee, L. (2022). Adolescents’ media usage and self-reported exposure to advertising across six countries: implications for less healthy food and beverage marketing. BMJ Open, 12(5), e058913. https://doi.org/10.1136/bmjopen-2021-058913
4
Nagata, J., Cortez, C., Iyer, P., Ganson, K., Chu, J., & Conroy, A. (2022). Parent-Adolescent Discrepancies in Adolescent Recreational Screen Time Reporting During the Coronavirus Disease 2019 Pandemic. Academic Pediatrics, 22(3), 413-421. https://doi.org/10.1016/j.acap.2021.12.008
5
Xu, L., Song, K., Deng, H., Geng, X., Zhang, J., Fang, X., … & Zhang, J. (2025). Beyond screen time: The core influences of problematic screen use on adolescent development networks. Journal of Behavioral Addictions, 14(2), 724-737. https://doi.org/10.1556/2006.2025.00035
6
Bezemer, W., Born, M., & Leerkes, A. (2023). Addressing Ethnic Differences in the Validity of Self-reported Criminal Behaviour Through a Social Desirability Measure. Journal of Quantitative Criminology, 40(2), 257-284. https://doi.org/10.1007/s10940-023-09567-y
7
Brenner, P. and DeLamater, J. (2016). Lies, Damned Lies, and Survey Self-Reports? Identity as a Cause of Measurement Bias. Social Psychology Quarterly, 79(4), 333-354. https://doi.org/10.1177/0190272516628298
8
Burke, M. and Carman, K. (2017). You can be too thin (but not too tall): Social desirability bias in self-reports of weight and height. Economics & Human Biology, 27, 198-222. https://doi.org/10.1016/j.ehb.2017.06.002
9
Buttenheim, A., Goldman, N., & Pebley, A. (2013). Underestimation of Adolescent Obesity. Nursing Research, 62(3), 195-202. https://doi.org/10.1097/nnr.0b013e318286b790
10
Chang, L. and Krosnick, J. (2009). National Surveys Via Rdd Telephone Interviewing Versus the Internet. Public Opinion Quarterly, 73(4), 641-678. https://doi.org/10.1093/poq/nfp075
11
Clarke, P., Sastry, N., Duffy, D., & Ailshire, J. (2014). Accuracy of Self-Reported Versus Measured Weight Over Adolescence and Young Adulthood: Findings From the National Longitudinal Study of Adolescent Health, 1996-2008. American Journal of Epidemiology, 180(2), 153-159. https://doi.org/10.1093/aje/kwu133
12
DeBell, M., Krosnick, J., Gera, K., Yeager, D., & McDonald, M. (2018). The Turnout Gap in Surveys: Explanations and Solutions. Sociological Methods & Research, 49(4), 1133-1162. https://doi.org/10.1177/0049124118769085
13
Durmaz, A., Dursun, İ., & Kabadayı, E. (2020). Mitigating the Effects of Social Desirability Bias in Self-Report Surveys., 146-185. https://doi.org/10.4018/978-1-7998-1025-4.ch007
14
Kennes, L., Lucassen, D., Vaes, A., Wagemakers, A., Kalinauskaite, I., Feskens, E., … & Brouwer‐Brolsma, E. (2025). Traqq®-Z. Design of the Evaluation Study on Accuracy, Usability, and User perspectives of an ecological momentary dietary assessment app among Dutch Adolescents (Ages 12-18) (Preprint). Jmir Research Protocols. https://doi.org/10.2196/70194
15
Kim, S. and Kim, S. (2013). National Culture and Social Desirability Bias in Measuring Public Service Motivation. Administration & Society, 48(4), 444-476. https://doi.org/10.1177/0095399713498749
16
Kosgolla, J., Smith, D., Begum, S., & Reinhart, C. (2023). Assessing the self-reported honesty threshold in adolescent epidemiological research: comparing supervised machine learning and inferential statistical techniques. BMC Medical Research Methodology, 23(1). https://doi.org/10.1186/s12874-023-02035-y
17
Latkin, C., Tuyet, N., Hà, T., Sripaipan, T., Zelaya, C., Minh, N., … & Go, V. (2016). Social Desirability Response Bias and Other Factors That May Influence Self-Reports of Substance Use and HIV Risk Behaviors: A Qualitative Study of Drug Users in Vietnam. Aids Education and Prevention, 28(5), 417-425. https://doi.org/10.1521/aeap.2016.28.5.417
18
Li, D., Hyde, A., & Zeng, Y. (2012). Impacts of social desirability response bias on sexuality care of cancer patients among Chinese nurses. Open Journal of Nursing, 02(04), 341-345. https://doi.org/10.4236/ojn.2012.24050
19
McCullagh, M. and Rosemberg, M. (2015). Social Desirability Bias in Self-Reporting of Hearing Protector Use among Farm Operators. The Annals of Occupational Hygiene, 59(9), 1200-1207. https://doi.org/10.1093/annhyg/mev046
20
Ng, Z., Lin, S., Niu, L., & Cipriano, C. (2024). Measurement Invariance of the Children’s Social Desirability Scale–Short Version (CSD-S) Across Gender, Grade Level, and Race/Ethnicity. Assessment, 32(3), 394-404. https://doi.org/10.1177/10731911241245789
21
Paunonen, S. and LeBel, E. (2012). Socially desirable responding and its elusive effects on the validity of personality assessments.. Journal of Personality and Social Psychology, 103(1), 158-175. https://doi.org/10.1037/a0028165
22
Pérez, A., Gabriel, K., Nehme, E., Mandell, D., & Hoelscher, D. (2015). Measuring the bias, precision, accuracy, and validity of self-reported height and weight in assessing overweight and obesity status among adolescents using a surveillance system. International Journal of Behavioral Nutrition and Physical Activity, 12(S1). https://doi.org/10.1186/1479-5868-12-s1-s2
23
Visschers, J., Jaspaert, E., & Vervaeke, G. (2015). Social Desirability in Intimate Partner Violence and Relationship Satisfaction Reports: An Exploratory Analysis. Journal of Interpersonal Violence, 32(9), 1401-1420. https://doi.org/10.1177/0886260515588922
24
Vu, A., Pham, K., Tran, N., & Ahmed, S. (2013). The Influence of Social Desirability on Self-Reported Sexual Behavior in HIV Survey in Rural Ethiopia. World Journal of Aids, 03(04), 345-349. https://doi.org/10.4236/wja.2013.34044
25
Wu, J. and Watkins, D. (2009). DEVELOPMENT AND VALIDATION OF A CHINESE VERSION OF THE SELF-CONCEPT CLARITY SCALE. Psychologia, 52(1), 67-79. https://doi.org/10.2117/psysoc.2009.67
26
Zimmerman, M., Rowe, K., Tuttle, L., & Bryant, A. (1997). Validity of Adolescents’ Report of Maternal Age. American Journal of Community Psychology, 25(6), 887-891. https://doi.org/10.1023/a:1022221430914
27
(2024). IoT Based Football Player Training Session Simulation. Iraqi Journal of Computer Communication Control and System Engineering, 97-108. https://doi.org/10.33103/uot.ijccce.24.4.8
28
Beume, N. (2009). S-Metric Calculation by Considering Dominated Hypervolume as Klee’s Measure Problem. Evolutionary Computation, 17(4), 477-492. https://doi.org/10.1162/evco.2009.17.4.17402
29
Black, P., Scarfone, K., & Souppaya, M. (2008). Cyber Security Metrics and Measures., 1-15. https://doi.org/10.1002/9780470087923.hhs440
30
Boumal, N. and Absil, P. (2011). Discrete regression methods on the cone of positive-definite matrices., 4232-4235. https://doi.org/10.1109/icassp.2011.5947287
31
Fedorchenko, E., Novikova, E., Kotenko, I., Gaifulina, D., Tushkanova, O., Levshun, D., … & Kolomeec, M. (2022). THE SECURITY AND PRIVACY MEASURING SYSTEM FOR THE INTERNET OF THINGS DEVICES. Voprosy Kiberbezopasnosti, (5(51)), 28-46. https://doi.org/10.21681/2311-3456-2022-5-28-46
32
Garofalakis, J., Giannakou, J., Giannakoudi, T., & Sakkopoulos, E. (2007). Promo., 1-7. https://doi.org/10.1145/1352694.1352725
33
Heikka, T., Ometto, G., Montesano, G., Rowe, S., Jansonius, N., & Crabb, D. (2020). Testing a phantom eye under various signal-to-noise ratio conditions using eleven different OCT devices. Biomedical Optics Express, 11(3), 1306. https://doi.org/10.1364/boe.383103
34
Krosman, K. and Sosnowski, J. (2021). Correlating Time Series Signals and Event Logs in Embedded Systems. Sensors, 21(21), 7128. https://doi.org/10.3390/s21217128
35
Memarzadeh, M., Johnson, D., Gardner, W., & Gao, L. (2006). Maximizing the Fidelity of Log Signals Transmitted via Digital Telemetry.. https://doi.org/10.2118/102819-ms
36
Nuutinen, M. and Oittinen, P. (2009). Method for measuring the objective quality of the TV-out function of mobile handsets.. https://doi.org/10.1117/12.805425
37
Parsa, V., Pourmand, N., Cowley, A., Gupta, M., & Forrester, C. (2010). Objective and subjective speech quality evaluation of wideband noise reduction algorithms.. The Journal of the Acoustical Society of America, 128(4_Supplement), 2325-2325. https://doi.org/10.1121/1.3508205
38
Reddy, R. (2024). Predictive Reliability Engineering for IoT Devices Using Deep Learning-Driven Telemetry Analytics and Observability-First SRE Methodologies. American International Journal of Computer Science and Technology, 6, 43-56. https://doi.org/10.63282/3117-5481/aijcst-v6i6p105
39
Simone, D. (1971). U.S. metric study interim report – commercial weights and measures.. https://doi.org/10.6028/nbs.sp.345-3
40
Singh, S., Bible, J., Liu, Z., Zhang, Z., & Singapogu, R. (2021). Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?. Frontiers in Robotics and Ai, 8. https://doi.org/10.3389/frobt.2021.625003
41
Thorpe, J., Forchhammer, B., & Maier, A. (2018). Development of a sensor-based behavioural monitoring solution to support dementia care (Preprint).. https://doi.org/10.2196/preprints.12013
42
Thottungal, R. (2025). Data Acquisition and Transmission System for Energy Boat. Interantional Journal of Scientific Research in Engineering and Management, 09(03), 1-9. https://doi.org/10.55041/ijsrem43082
43
Tsai, N., Goodwin, J., Semler, M., Kothera, R., Horn, M., Wolf, B., … & Garner, D. (2017). Development of a Non-Invasive Blink Reflexometer. Ieee Journal of Translational Engineering in Health and Medicine, 5, 1-4. https://doi.org/10.1109/jtehm.2017.2782669
44
Vakanski, A., Ferguson, J., & Lee, S. (2017). Metrics for Performance Evaluation of Patient Exercises during Physical Therapy. International Journal of Physical Medicine & Rehabilitation, 05(03). https://doi.org/10.4172/2329-9096.1000403
45
Anglim, J., Horwood, S., Smillie, L., Marrero, R., & Wood, J. (2019). Predicting Psychological and Subjective Well-Being from Personality: A Meta-Analysis.. https://doi.org/10.31234/osf.io/gupxj
46
Anglim, J., Horwood, S., Smillie, L., Marrero, R., & Wood, J. (2020). Predicting psychological and subjective well-being from personality: A meta-analysis.. Psychological Bulletin, 146(4), 279-323. https://doi.org/10.1037/bul0000226
47
Aoki, Y., Takaesu, Y., Matsumoto, Y., Sakurai, H., Tsuboi, T., Okajima, I., … & Zimmerman, M. (2024). A psychometric analysis of the Japanese version of the clinically useful depression outcome scale supplemented with questions for the DSM‐5 anxious distress specifier (CUDOS‐A). Neuropsychopharmacology Reports, 44(3), 526-533. https://doi.org/10.1002/npr2.12432
48
Bushnell, D., McCarrier, K., Bush, E., Abraham, L., Jamieson, C., McDougall, F., … & Coons, S. (2019). Symptoms of Major Depressive Disorder Scale: Performance of a Novel Patient-Reported Symptom Measure. Value in Health, 22(8), 906-915. https://doi.org/10.1016/j.jval.2019.02.010
49
Cai, X., Ye, Q., Ni, K., Zhu, L., Zhang, Q., Yin, M., … & Li, B. (2024). Chinese version of the Perth Alexithymia Questionnaire: psychometric properties and clinical applications. General Psychiatry, 37(2), e101281. https://doi.org/10.1136/gpsych-2023-101281
50
Campos, A., Velzen, L., Veltman, D., Pozzi, E., Ambrogi, S., Ballard, E., … & Rentería, M. (2021). Concurrent validity and reliability of suicide risk assessment instruments: A meta analysis of 20 instruments across 27 international cohorts.. https://doi.org/10.1101/2021.09.15.21263562
51
Chen, Y., Liang, S., Lu, M., & Chen, V. (2024). Reliability and Validity of the Chen Depression Scale: A Preliminary Study. Taiwanese Journal of Psychiatry, 38(2), 75-80. https://doi.org/10.4103/tpsy.tpsy_16_24
52
Ebesutani, C., Bernstein, A., Nakamura, B., Chorpita, B., & Weisz, J. (2009). A Psychometric Analysis of the Revised Child Anxiety and Depression Scale Parent Version in a Clinical Sample. Journal of Abnormal Child Psychology, 38(2), 249-260. https://doi.org/10.1007/s10802-009-9363-8
53
Gelaye, B., Williams, M., Lemma, S., Deyessa, N., Bahretibeb, Y., Shibre, T., … & Zhou, X. (2013). Diagnostic Validity of the Composite International Diagnostic Interview (CIDI) Depression Module in an East African Population. The International Journal of Psychiatry in Medicine, 46(4), 387-405. https://doi.org/10.2190/pm.46.4.e
54
Ghapanch, A., Abasi, I., Bitarafan, M., Zarabi, H., Derakhshan, F., Derakhshan, M., … & Shamsi, A. (2022). The Psychometric Evaluation of Somatic Symptom Scale-8 in Patients With Major Depressive Disorder. Practice in Clinical Psychology, 10(1), 69-78. https://doi.org/10.32598/jpcp.10.1.812.4
55
Hyphantis, T., Kotsis, K., Voulgari, P., Tsifetaki, N., Creed, F., & Drosos, A. (2011). Diagnostic accuracy, internal consistency, and convergent validity of the Greek version of the patient health questionnaire 9 in diagnosing depression in rheumatologic disorders. Arthritis Care & Research, 63(9), 1313-1321. https://doi.org/10.1002/acr.20505
56
Kim, J., Hong, J., Kim, S., Kang, H., & Lee, Y. (2016). Development of a Korean Version of the Perceived Deficits Questionnaire-Depression for Patients with Major Depressive Disorder. Clinical Psychopharmacology and Neuroscience, 14(1), 26-32. https://doi.org/10.9758/cpn.2016.14.1.26
57
Klaufus, L., Verlinden, E., Wal, M., Kösters, M., Cuijpers, P., & Chinapaw, M. (2020). Psychometric Evaluation of Two Short Versions of the Revised Child Anxiety and Depression Scale.. https://doi.org/10.21203/rs.2.13956/v3
58
Lowe, G. (2021). Validation of the Major Depressive Disorder Subscale (MDDS) of the Revised Child Anxiety & Depression Scale (RCADS) in a Sample of Jamaican and Barbadian Elementary School Children. Caribbean Medical Journal. https://doi.org/10.48107/cmj2021.04.007
59
Ma, M., Xiao, C., Ou, W., Lv, G., Huang, M., Zhao, X., … & Zhang, Y. (2023). Psychometric property study of the Affective Lability Scale-short form in Chinese patients with mood disorders. Frontiers in Psychiatry, 14. https://doi.org/10.3389/fpsyt.2023.1160791
60
Monahan, P., Shacham, E., Reece, M., Kroenke, K., Ong’or, W., Omollo, O., … & Ojwang, C. (2008). Validity/Reliability of PHQ-9 and PHQ-2 Depression Scales Among Adults Living with HIV/AIDS in Western Kenya. Journal of General Internal Medicine, 24(2), 189-197. https://doi.org/10.1007/s11606-008-0846-z
61
Piqueras, J., Pineda, D., Martín-Vivar, M., & Sandín, B. (2017). Confirmatory factor analysis and psychometric properties of the Revised Child Anxiety and Depression Scale−30 (RCADS-30) in clinical and non-clinical samples. Revista De Psicopatología Y Psicología Clínica, 22(3), 183. https://doi.org/10.5944/rppc.vol.22.num.3.2017.19332
62
Sakunpong, N. and Ritkumrop, K. (2021). Psychometric Properties of the Thai Version Psychological Well-Being Scale and the Factors Related to among Thai Patients with Major Depressive Disorder. Depression Research and Treatment, 2021, 1-7. https://doi.org/10.1155/2021/2592548
63
Vogeli, J., Hooker, S., Everhart, K., & Kaplan, P. (2018). Psychometric properties of the postpartum depression screening scale beyond the postpartum period. Research in Nursing & Health, 41(2), 185-194. https://doi.org/10.1002/nur.21861
64
Vojvodić, P., Andonov, A., Stevanović, D., Peruničić, I., Mihajlović, G., & Vojvodic, J. (2020). Montgomery-Asberg depression rating scale in clinical practice: Psychometric properties on Serbian patients. Vojnosanitetski Pregled, 77(11), 1119-1125. https://doi.org/10.2298/vsp171017176v
65
Walter, L., Meresman, J., Kramer, T., & Evans, R. (2003). The Depression Arkansas scale: A validation study of a new brief depression scale in an HMO. Journal of Clinical Psychology, 59(4), 465-481. https://doi.org/10.1002/jclp.10137
66
Yılmaz, A., Sungur, M., Konkan, R., & Şenormancı, Ö. (2014). Psychometric Properties of the Metacognitions Questionnaires about Rumination in Clinical and Non-clinical Turkish Samples. Turkish Journal of Psychiatry. https://doi.org/10.5080/u7879
67
Bartholomew, K. and Horowitz, L. (1991). Attachment styles among young adults: A test of a four-category model.. Journal of Personality and Social Psychology, 61(2), 226-244. https://doi.org/10.1037/0022-3514.61.2.226
68
Bautista-Valdivia, J., Jiménez, J., & Aranda, B. (2024). Attachment and mental health in families of native people: A cross-sectional study. Interacciones Revista De Avances en Psicología, e438. https://doi.org/10.24016/2024.v10.438
69
Brown, J. and Trevethan, R. (2010). Shame, Internalized Homophobia, Identity Formation, Attachment Style, and the Connection to Relationship Status in Gay Men. American Journal of Men S Health, 4(3), 267-276. https://doi.org/10.1177/1557988309342002
70
Chang, C., Ohannessian, C., Ewing, E., Kobak, R., Diamond, G., & Herres, J. (2019). Attachment and Parent-Adolescent Discrepancies in Reports of Family Functioning among Suicidal Adolescents. Journal of Child and Family Studies, 29(1), 227-236. https://doi.org/10.1007/s10826-019-01566-7
71
Demby, K. Family Interaction Patterns, Child Attachment, and Child Emotional Adjustment.. https://doi.org/10.12794/metadc699925
72
Demby, K., Riggs, S., & Kaminski, P. (2015). Attachment and Family Processes in Children’s Psychological Adjustment in Middle Childhood. Family Process, 56(1), 234-249. https://doi.org/10.1111/famp.12145
73
Dinero, R., Conger, R., Shaver, P., Widaman, K., & Larsen‐Rife, D. (2008). Influence of family of origin and adult romantic partners on romantic attachment security.. Journal of Family Psychology, 22(4), 622-632. https://doi.org/10.1037/a0012506
74
Gandhi, A., Luyckx, K., Molenberghs, G., Baetens, I., Goossens, L., Maitra, S., … & Claes, L. (2019). Maternal and peer attachment, identity formation, and non-suicidal self-injury: a longitudinal mediation study. Child and Adolescent Psychiatry and Mental Health, 13(1). https://doi.org/10.1186/s13034-019-0267-2
75
Kobak, R. and Sceery, A. (1988). Attachment in Late Adolescence: Working Models, Affect Regulation, and Representations of Self and Others. Child Development, 59(1), 135-146. https://doi.org/10.1111/j.1467-8624.1988.tb03201.x
76
Lease, S. (2002). A Model of Depression in Adult Children of Alcoholics and Nonalcoholics. Journal of Counseling & Development, 80(4), 441-451. https://doi.org/10.1002/j.1556-6678.2002.tb00211.x
77
Minić, J. (2015). Family sense of coherence and family and partner affective attachment in adolescents. Zbornik Radova Filozofskog Fakulteta U Pristini, (45-4), 251-276. https://doi.org/10.5937/zrffp45-7938
78
Riggs, S., Raiche, E., Creech, S., McGuffin, J., & Romero, D. (2020). Attachment, couple communication, and family functioning in relation to psychological distress among service members and veterans.. Couple and Family Psychology Research and Practice, 9(4), 239-255. https://doi.org/10.1037/cfp0000154
79
Tartaro, G., Baptista, M., Peixoto, E., & Franco, V. (2023). Self-esteem and depressed thoughts can be explained by attachment and family support.. https://doi.org/10.31234/osf.io/9f8kc
80
Ying, L., Zhang, S., & Jia, X. (2022). Peer attachment and self‐esteem mediate the relationship between family function and social anxiety in migrant children in China. Child Care Health and Development, 49(3), 563-571. https://doi.org/10.1111/cch.13072
81
Zhou, Z. (2025). The Impact of Family of Origin on Romantic Relationships: A Psychological Perspective. Lecture Notes in Education Psychology and Public Media, 81(1), 70-78. https://doi.org/10.54254/2753-7048/2025.20444
82
Zhu, H., Fu, H., Liu, H., Wang, B., & Zhong, X. (2025). The Protective Role of Caring Parenting Styles in Adolescent Bullying Victimization: The Effects of Family Function and Constructive Conflict Resolution. Behavioral Sciences, 15(7), 982. https://doi.org/10.3390/bs15070982
83
Zisk, A., Abbott, C., Bounoua, N., Diamond, G., & Kobak, R. (2019). Parent–teen communication predicts treatment benefit for depressed and suicidal adolescents.. Journal of Consulting and Clinical Psychology, 87(12), 1137-1148. https://doi.org/10.1037/ccp0000457
84
Zulkefly, N. and Wilkinson, R. (2014). Measuring Specific Attachment Relationships of Mother, Father and Peer in Malaysian Adolescents. Child Indicators Research, 8(4), 767-788. https://doi.org/10.1007/s12187-014-9271-5
85
(2021). 11.B. Workshop: COPERS – an international longitudinal study on coping and resilience during the COVID-19 pandemic. European Journal of Public Health, 31(Supplement_3). https://doi.org/10.1093/eurpub/ckab164.805
86
Jiang, R., Westwater, M., Noble, S., Rosenblatt, M., Dai, W., Qi, S., … & Scheinost, D. (2022). Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank. BMC Medicine, 20(1). https://doi.org/10.1186/s12916-022-02490-2
87
Joensen, A., Danielsen, S., Andersen, P., Groot, J., & Strandberg‐Larsen, K. (2021). The impact of the initial and 2
88
nd
89
national COVID-19 lockdown on mental health in young people with and without pre-existing depressive symptoms.. https://doi.org/10.1101/2021.11.03.21265861
90
Krieger, N. (2017). Religious Service Attendance and Suicide Rates. Jama Psychiatry, 74(2), 197. https://doi.org/10.1001/jamapsychiatry.2016.2744
91
Lewis, G., Dykxhoorn, J., Karlsson, H., Khandaker, G., Lewis, G., Dalman, C., … & Kirkbride, J. (2020). Assessment of the Role of IQ in Associations Between Population Density and Deprivation and Nonaffective Psychosis. Jama Psychiatry, 77(7), 729. https://doi.org/10.1001/jamapsychiatry.2020.0103
92
Moxley, E., Habtezgi, D., Varothai, K., Henert, S., Kowal, R., Bode, B., … & Budwhani, S. (2024). Abstract P218: Moderate to Vigorous Physical Activity to Improve Cardiovascular and Mental Health in Sedentary Middle-Aged Adults. Circulation, 149(Suppl_1). https://doi.org/10.1161/circ.149.suppl_1.p218
93
Rizanaj, N. and Gavazaj, F. (2024). Effects of Depressive and Anxiety Behaviors in Patients aged 30-75+ Who Have Experienced Covid-19.. https://doi.org/10.20944/preprints202406.1717.v1
94
Sampasa‐Kanyinga, H., Chaput, J., Goldfield, G., Janssen, I., Wang, J., Hamilton, H., … & Colman, I. (2020). The Canadian 24-Hour Movement Guidelines and Psychological Distress among Adolescents: Les Directives canadiennes en matière de mouvement sur 24 heures et la détresse psychologique chez les adolescents. The Canadian Journal of Psychiatry, 66(7), 624-633. https://doi.org/10.1177/0706743720970863
95
Sewall, C. (2021). Objectively measured digital technology use during the COVID-19 pandemic: Impact on depression, anxiety, and suicidal ideation among young adults.. https://doi.org/10.31234/osf.io/nja64
96
VanderWeele, T., Li, S., & Kawachi, I. (2017). Religious Service Attendance and Suicide Rates Reply. Jama Psychiatry, 74(2), 197. https://doi.org/10.1001/jamapsychiatry.2016.2780
97
Vowels, M., Vowels, L., & Miller, J. (2024). Longitudinal evidence over 2 years of the pandemic shows that poor mental health in people living with obesity may be underestimated. Plos One, 19(7), e0305627. https://doi.org/10.1371/journal.pone.0305627
98
Apostolopoulos, M., Hnatiuk, J., Maple, J., Olander, E., Brennan, L., Pligt, P., … & Teychenne, M. (2021). Influences on physical activity and screen time amongst postpartum women with heightened depressive symptoms: a qualitative study. BMC Pregnancy and Childbirth, 21(1). https://doi.org/10.1186/s12884-021-03847-w
99
Dapp, U., Minder, C., Golgert, S., Klugmann, B., Neumann, L., & Renteln‐Kruse, W. (2020). The inter-relationship between depressed mood, functional decline and disability over a 10-year observational period within the Longitudinal Urban Cohort Ageing Study (LUCAS). Journal of Epidemiology & Community Health, 75(5), 450-457. https://doi.org/10.1136/jech-2020-214168
100
He, J., Liu, M., Zhang, Z., Fang, M., Wu, L., Haisheng, W., … & Li, Z. (2025). The longitudinal bidirectional association between cardiovascular disease and depressive symptoms among middle-aged and elderly adults: evidence from a nationwide cohort study in China. Frontiers in Psychiatry, 16. https://doi.org/10.3389/fpsyt.2025.1559092
101
Houghton, S., Lawrence, D., Hunter, S., Rosenberg, M., Zadow, C., Wood, L., … & Shilton, T. (2018). Reciprocal Relationships between Trajectories of Depressive Symptoms and Screen Media Use during Adolescence. Journal of Youth and Adolescence, 47(11), 2453-2467. https://doi.org/10.1007/s10964-018-0901-y
102
Khouja, J., Munafò, M., Tilling, K., Wiles, N., Joinson, C., Etchells, P., … & Cornish, R. (2019). Is screen time associated with anxiety or depression in young people? Results from a UK birth cohort. BMC Public Health, 19(1). https://doi.org/10.1186/s12889-018-6321-9
103
Li, S., Batterham, P., Whitton, A., Maston, K., Khan, A., Christensen, H., … & Werner‐Seidler, A. (2025). Cross‐sectional and longitudinal associations of screen time with adolescent depression and anxiety. British Journal of Clinical Psychology, 64(4), 873-887. https://doi.org/10.1111/bjc.12547
104
Meyer, J., O’Connor, J., McDowell, C., Lansing, J., Brower, C., & Herring, M. (2021). High Sitting Time Is a Behavioral Risk Factor for Blunted Improvement in Depression Across 8 Weeks of the COVID-19 Pandemic in April–May 2020. Frontiers in Psychiatry, 12. https://doi.org/10.3389/fpsyt.2021.741433
105
Mougharbel, F., Chaput, J., Sampasa‐Kanyinga, H., Colman, I., Leatherdale, S., Patte, K., … & Goldfield, G. (2023). Longitudinal associations between different types of screen use and depression and anxiety symptoms in adolescents. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1101594
106
Neville, R., McArthur, B., Eirich, R., Lakes, K., & Madigan, S. (2021). Bidirectional associations between screen time and children’s externalizing and internalizing behaviors. Journal of Child Psychology and Psychiatry, 62(12), 1475-1484. https://doi.org/10.1111/jcpp.13425
107
Patte, K., Faulkner, G., Qian, W., Duncan, M., & Leatherdale, S. (2020). Are one-year changes in adherence to the 24-hour movement guidelines associated with depressive symptoms among youth?.. https://doi.org/10.21203/rs.2.20569/v1
108
Xiang, M., Liu, Y., Yamamoto, S., Mizoue, T., & Kuwahara, K. (2022). Association of Changes of lifestyle behaviors before and during the COVID-19 pandemic with mental health: a longitudinal study in children and adolescents. International Journal of Behavioral Nutrition and Physical Activity, 19(1). https://doi.org/10.1186/s12966-022-01327-8
109
Xu, J., Baldwin, J., Hughes, A., Herbert, A., Munafò, M., & Howe, L. (2024). Exploring genetic confounding of the associations between excessive screen time and depressive symptoms in adolescence and early adulthood.. https://doi.org/10.1101/2024.12.02.24318295
110
Abe, H., Takeoka, K., Fuchisawa, Y., Koyama, K., & Koseki, S. (2021). A New Dose-Response Model for Estimating the Infection Probability of Campylobacter jejuni Based on the Key Events Dose-Response Framework. Applied and Environmental Microbiology, 87(20). https://doi.org/10.1128/aem.01299-21
111
Calabrese, E. (2008). Hormesis: Why it is important to toxicology and toxicologists. Environmental Toxicology and Chemistry, 27(7), 1451-1474. https://doi.org/10.1897/07-541.1
112
Chernoff, E., Russell, G., Vashchyshyn, I., Neufeld, H., & Banting, N. (2017). There is no evidence for order mattering; therefore, order does not matter. Avances De Investigación en Educación Matemática, (11), 5-24. https://doi.org/10.35763/aiem.v1i11.179
113
Cohen, B. (2002). Correspondence. Journal of the Royal Statistical Society Series a (Statistics in Society), 165(3), 567-568. https://doi.org/10.1111/1467-985x.t01-2-00253
114
Dobrzyński, M., Nguyen, L., Birtwistle, M., Kriegsheim, A., Blanco, A., Cheong, A., … & Kholodenko, B. (2014). Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses. Journal of the Royal Society Interface, 11(98). https://doi.org/10.1098/rsif.2014.0383
115
Hansson, S. (2011). Radiation Protection Sorting Out the Arguments. Philosophy & Technology, 24(3), 363-368. https://doi.org/10.1007/s13347-011-0036-5
116
Jongbloet, P. (2004). Over-ripeness ovopathy: A challenging hypothesis for sex ratio modulation. Human Reproduction, 19(4), 769-774. https://doi.org/10.1093/humrep/deh136
117
Jongbloet, P., Zielhuis, G., Groenewoud, H., & Jong, P. (2001). The secular trends in male:female ratio at birth in postwar industrialized countries.. Environmental Health Perspectives, 109(7), 749-752. https://doi.org/10.1289/ehp.01109749
118
Lipfert, F. (1999). The use and misuse of surrogate variables in environmental epidemiology. Journal of Environmental Medicine, 1(4), 267-278. https://doi.org/10.1002/jem.40
119
Price, W. and Shafii, B. (2005). BAYESIAN ANALYSIS OF DOSE-RESPONSE CALIBRATION CURVES. Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1135
120
Sacks, B. and Siegel, J. (2017). Preserving the Anti-Scientific Linear No-Threshold Myth: Authority, Agnosticism, Transparency, and the Standard of Care. Dose-Response, 15(3), 155932581771783. https://doi.org/10.1177/1559325817717839
121
Shrader‐Frechette, K. (2008). Ideological toxicology: invalid logic, science, ethics about low-dose pollution. Human & Experimental Toxicology, 27(8), 647-657. https://doi.org/10.1177/0960327108098491
122
Sutou, S. (2017). The 10th anniversary of the publication of genes and environment: memoir of establishing the Japanese environmental mutagen society and a proposal for a new collaborative study on mutagenic hormesis. Genes and Environment, 39(1). https://doi.org/10.1186/s41021-016-0073-5
123
Wang, H., Burgei, W., & Zhou, H. (2017). Interpreting Dose-Response Relation for Exposure to Multiple Sound Impulses in the Framework of Immunity. Health, 09(13), 1817-1842. https://doi.org/10.4236/health.2017.913132
124
Zeise, L., Wilson, R., & Crouch, E. (1987). Dose-Response Relationships for Carcinogens: A Review. Environmental Health Perspectives, 73, 259. https://doi.org/10.2307/3430618
125
Zeise, L., Wilson, R., & Crouch, E. (1987). Dose-response relationships for carcinogens: a review.. Environmental Health Perspectives, 73, 259-306. https://doi.org/10.1289/ehp.8773259
126
Ansari, S., Iqbal, N., Azeem, A., & Danyal, K. (2024). Improving Well-Being Through Digital Detoxification Among Social Media Users: A Systematic Review and Meta-Analysis. Cyberpsychology Behavior and Social Networking, 27(11), 753-770. https://doi.org/10.1089/cyber.2023.0742
127
Calabrese, E. (2007). Hormesis: Why it is Important to Toxicology and Toxicologists. Environmental Toxicology and Chemistry, preprint(2008), 1. https://doi.org/10.1897/07-541
128
Chari, S. (2026). Social Isolation Among the Connected Generation: A Review. Premier Journal of Social Science. https://doi.org/10.70389/pjss.100013
129
Cologne, J. (2025). Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications. Carcinogenesis, 46(3). https://doi.org/10.1093/carcin/bgaf043
130
Du, M., Zhao, C., Hu, H., Ding, N., He, J., Tian, W., … & Zhang, G. (2024). Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis. BMC Psychology, 12(1). https://doi.org/10.1186/s40359-024-01705-w
131
Gruenewald, P. and Mair, C. (2015). Heterogeneous dose–response and college student drinking: examining problem risks related to low drinking levels. Addiction, 110(6), 945-954. https://doi.org/10.1111/add.12887
132
Houghton, S., Lawrence, D., Hunter, S., Rosenberg, M., Zadow, C., Wood, L., … & Shilton, T. (2018). Reciprocal Relationships between Trajectories of Depressive Symptoms and Screen Media Use during Adolescence. Journal of Youth and Adolescence, 47(11), 2453-2467. https://doi.org/10.1007/s10964-018-0901-y
133
Jongbloet, P., Zielhuis, G., Groenewoud, H., & Jong, P. (2001). The Secular Trends in Male:Female Ratio at Birth in Postwar Industrialized Countries. Environmental Health Perspectives, 109(7), 749. https://doi.org/10.2307/3454793
134
Jongbloet, P., Zielhuis, G., Groenewoud, H., & Jong, P. (2001). The secular trends in male:female ratio at birth in postwar industrialized countries.. Environmental Health Perspectives, 109(7), 749-752. https://doi.org/10.1289/ehp.01109749
135
Khan, T., Chiavaroli, L., Zurbau, A., & Sievenpiper, J. (2019). A lack of consideration of a dose–response relationship can lead to erroneous conclusions regarding 100% fruit juice and the risk of cardiometabolic disease. European Journal of Clinical Nutrition, 73(12), 1556-1560. https://doi.org/10.1038/s41430-019-0514-x
136
Makris, S., Thompson, C., Euling, S., Selevan, S., & Sonawane, B. (2008). A lifestage‐specific approach to hazard and dose‐response characterization for children’s health risk assessment. Birth Defects Research Part B Developmental and Reproductive Toxicology, 83(6), 530-546. https://doi.org/10.1002/bdrb.20176
137
Modecki, K., Duvenage, M., Uink, B., Barber, B., & Donovan, C. (2021). Adolescents’ Online Coping: When Less Is More but None Is Worse. Clinical Psychological Science, 10(3), 467-481. https://doi.org/10.1177/21677026211028983
138
Nascarella, M. and Calabrese, E. (2012). A Method to Evaluate Hormesis in Nanoparticle Dose-Responses. Dose-Response, 10(3). https://doi.org/10.2203/dose-response.10-025.nascarella
139
Przybylski, A. and Weinstein, N. (2017). A Large-Scale Test of the Goldilocks Hypothesis. Psychological Science, 28(2), 204-215. https://doi.org/10.1177/0956797616678438
140
Serrano, B., Spiers, A., Ruotong, L., Gangadia, S., Toledano, M., & Simplicio, M. (2022). Impact of mobile phones and wireless devices use on children and adolescents’ mental health: a systematic review. European Child & Adolescent Psychiatry, 33(6), 1621-1651. https://doi.org/10.1007/s00787-022-02012-8
141
Sittichai, R. and Smith, P. (2020). Information Technology Use and Cyberbullying Behavior in South Thailand: A Test of the Goldilocks Hypothesis. International Journal of Environmental Research and Public Health, 17(19), 7122. https://doi.org/10.3390/ijerph17197122
142
Wu, D., Liu, M., Li, D., & Yin, H. (2024). The longitudinal relationship between loneliness and both social anxiety and mobile phone addiction among rural left‐behind children: A cross‐lagged panel analysis. Journal of Adolescence, 96(5), 969-982. https://doi.org/10.1002/jad.12309
143
Wu-Ouyang, B. (2023). More Mobile Connectedness, Less Well-Being?. Journal of Media Psychology Theories Methods and Applications, 35(5), 291-302. https://doi.org/10.1027/1864-1105/a000388
144
Yang, Y., Li, X., Zhang, Q., Zhang, X., & Liu, J. (2023). Research on the Relationship between Smartphone Dependency and Feelings of Loneliness: Novel Insights from the “Theory of Usage Ineffectiveness”. Applied & Educational Psychology, 4(11). https://doi.org/10.23977/appep.2023.041118
145
Eamon, M. (2001). The Effects of Poverty on Children’s Socioemotional Development: An Ecological Systems Analysis. Social Work, 46(3), 256-266. https://doi.org/10.1093/sw/46.3.256
146
Ferguson, Y., Kasser, T., & Jahng, S. (2010). Differences in Life Satisfaction and School Satisfaction Among Adolescents From Three Nations: The Role of Perceived Autonomy Support. Journal of Research on Adolescence, 21(3), 649-661. https://doi.org/10.1111/j.1532-7795.2010.00698.x
147
Francis, A. Exploring parental attitudes and behaviors towards involvement in STEM education: supporting learning across settings.. https://doi.org/10.17760/d20328901
148
Haryani, Y. (2025). Integrating Digital Literacy in the Ecological Environment of Child Development. Jurnal Ilmiah Mandala Education, 11(3), 675. https://doi.org/10.58258/jime.v11i3.9176
149
Hatzigianni, M., Stephenson, T., Harrison, L., Waniganayake, M., Li, H., Barblett, L., … & Irvine, S. (2023). The role of digital technologies in supporting quality improvement in Australian early childhood education and care settings. International Journal of Child Care and Education Policy, 17(1). https://doi.org/10.1186/s40723-023-00107-6
150
Holmarsdottir, H., Seland, I., & Hyggen, C. (2024). How Can We Understand the Everyday Digital Lives of Children and Young People?., 3-26. https://doi.org/10.1007/978-3-031-46929-9_1
151
Kuchynka, S., Eaton, A., & Rivera, L. (2022). Understanding and Addressing Gender‐Based Inequities in STEM: Research Synthesis and Recommendations for U.S. K‐12 Education. Social Issues and Policy Review, 16(1), 252-288. https://doi.org/10.1111/sipr.12087
152
Liu, S. (2023). YOUTH CYBERBULLYING – UNDERSTANDING CONTEXTUAL PATHS TO PREVENTION AND RESILIENCE.. https://doi.org/10.36315/2023v2end053
153
Navarro, J. and Tudge, J. (2022). Technologizing Bronfenbrenner: Neo-ecological Theory. Current Psychology, 42(22), 19338-19354. https://doi.org/10.1007/s12144-022-02738-3
154
Pavalachi, D. (2025). Social challenges of teenagers: problems and solutions from the perspective of educational counseling. Moldoscopie, (2(101)), 139-145. https://doi.org/10.52388/1812-2566.2024.2(101).14
155
Strauss, G. (2021). A Bioecosystem Theory of Negative Symptoms in Schizophrenia. Frontiers in Psychiatry, 12. https://doi.org/10.3389/fpsyt.2021.655471
156
Voydanoff, P. (2001). Incorporating Community into Work and Family Research: A Review of Basic Relationships. Human Relations, 54(12), 1609-1637. https://doi.org/10.1177/00187267015412003
157
Wachs, T. (2010). Viewing microsystem chaos through a Bronfenbrenner bioecological lens.., 97-112. https://doi.org/10.1037/12057-007
158
Wachs, T. (2015). Assessing Bioecological Influences., 1-36. https://doi.org/10.1002/9781118963418.childpsy421
159
Afzal, A., Khan, S., Daud, S., Ahmad, Z., & Butt, A. (2023). Addressing the Digital Divide: Access and Use of Technology in Education. Journal of Social Sciences Review, 3(2), 883-895. https://doi.org/10.54183/jssr.v3i2.326
160
Bucy, E. (2000). Social Access to the Internet. Harvard International Journal of Press/Politics, 5(1), 50-61. https://doi.org/10.1177/1081180×00005001005
161
Cabasan, R. (2024). Effective Technology Integration: Closing the Digital Gap among High School Students. JIP, 2(8). https://doi.org/10.69569/jip.2024.0295
162
Goto, R., WATANABE, Y., Yamazaki, A., Sugita, M., Takeda, S., Nakabayashi, M., … & Nakamura, Y. (2021). Can digital health technologies exacerbate the health gap? A clustering analysis of mothers’ opinions toward digitizing the maternal and child health handbook. SSM – Population Health, 16, 100935. https://doi.org/10.1016/j.ssmph.2021.100935
163
Hansen, J. and Reich, J. (2015). Democratizing education? Examining access and usage patterns in massive open online courses. Science, 350(6265), 1245-1248. https://doi.org/10.1126/science.aab3782
164
Ihm, J., Kim, Y., & Lee, C. (2024). Whom Does Remote Work Make Happy? The Digital Divide in Remote Workers’ Well-Being. Cyberpsychology Behavior and Social Networking, 27(8), 550-561. https://doi.org/10.1089/cyber.2023.0744
165
Kem, D. (2024). Digital Divide: Examining Socioeconomic Inequalities in Internet Access and Usage. International Journal for Multidisciplinary Research, 6(6). https://doi.org/10.36948/ijfmr.2024.v06i06.30386
166
Kucker, S. and Schneider, J. (2024). Social interactions offset the detrimental effects of digital media use on children’s vocabulary. Frontiers in Developmental Psychology, 2. https://doi.org/10.3389/fdpys.2024.1401736
167
Liao, J., Kumar, S., & Furuoka, F. (2025). Bridging the Digital Divide: How Internet Access Shapes Human Capital Development and Economic Inequality. E-Bangi Journal of Social Science and Humanities, 22(2). https://doi.org/10.17576/ebangi.2025.2202.03
168
Lin, F., Jin, L., & Chen, X. (2025). Digitalization, Psychological Well-Being, and the Third-Level Digital Divide: Survey Study During the COVID-19 Pandemic in China. Journal of Medical Internet Research, 27, e48195. https://doi.org/10.2196/48195
169
Mukherjee, P. (2025). BRIDGING THE DIGITAL DIVIDE: HARNESSING TECHNOLOGY FOR INCLUSIVE GROWTH IN INDIA. tssr, (Special), 176-179. https://doi.org/10.70096/tssr.250307031
170
Naftel, R., Safiano, N., Falola, M., Shannon, C., Wellons, J., & Johnston, J. (2013). Technology preferences among caregivers of children with hydrocephalus. Journal of Neurosurgery Pediatrics, 11(1), 26-36. https://doi.org/10.3171/2012.9.peds12208
171
Nair, P. (2025). Equity by Design – Using Digital Technology To Overcome Cardiovascular Health Disparities. Current Cardiology Reports, 27(1). https://doi.org/10.1007/s11886-025-02319-3
172
Nam, T. (2011). Whose e-democracy? The democratic divide in American electoral campaigns. Information Polity, 16(2), 131-150. https://doi.org/10.3233/ip-2011-0220
173
Owen, D. (2016). Digital Divide., 1-5. https://doi.org/10.1002/9781118541555.wbiepc176
174
Pettalongi, S., Londol, M., & Umboh, S. (2024). Disparities in Digital Education: Socioeconomic Barriers to Accessing Online Learning Resources. International Journal of Social and Human, 1(3), 181-189. https://doi.org/10.59613/be6gdv98
175
Puja, T. (2024). Education for all: Addressing the digital divide and socioeconomic disparities in modern schools. I-Manager S Journal on School Educational Technology, 20(2), 31. https://doi.org/10.26634/jsch.20.2.21211
176
Qaribilla, R., Indrajaya, K., & Mayawati, C. (2024). Digital Learning Inquality: The Role of Socioeconomic Status in Access to Online Education Resources. ijsh, 1(2), 51-58. https://doi.org/10.59613/55gdmt96
177
Vaidehi, R., Reddy, A., & Banerjee, S. (2021). Explaining Caste-based Digital Divide in India.. https://doi.org/10.48550/arxiv.2106.15917
178
Warschauer, M. (2007). A Teacher’s Place in the Digital Divide. Teachers College Record, 109(14), 147-166. https://doi.org/10.1177/016146810710901408
179
Acılar, A. and Sæbø, Ø. (2021). Towards understanding the gender digital divide: a systematic literature review. Global Knowledge Memory and Communication, 72(3), 233-249. https://doi.org/10.1108/gkmc-09-2021-0147
180
Alhazmi, H., Imran, A., & Alsheikh, M. (2021). Digital Divide and Social Dilemma of Privacy Preservation.. https://doi.org/10.48550/arxiv.2110.02669
181
Almeida, A., Alves, N., Delicado, A., & Carvalho, T. (2011). Children and digital diversity: From ‘unguided rookies’ to ‘self-reliant cybernauts’. Childhood, 19(2), 219-234. https://doi.org/10.1177/0907568211410897
182
Barroso, C., Kolotouchkina, O., Mañas-Viniegra, L., & Abad, M. (2022). ICT-Mediated Learning as a Form of Socio-Emotional Support for Older Adults. Interaction Design and Architecture(s), (54), 8-33. https://doi.org/10.55612/s-5002-054-001
183
Bhawra, J., Elsahli, N., & Patel, J. (2024). Applying Digital Technology to Understand Human Experiences of Climate Change Impacts on Food Security and Mental Health: Scoping Review. Jmir Public Health and Surveillance, 10, e54064. https://doi.org/10.2196/54064
184
Fung, K., Hung, S., Lai, D., Shum, M., Fung, H., & He, L. (2023). Access to Information and Communication Technology, Digital Skills, and Perceived Well-Being among Older Adults in Hong Kong. International Journal of Environmental Research and Public Health, 20(13), 6208. https://doi.org/10.3390/ijerph20136208
185
Halim, U., Febrina, D., Agustina, A., Hidayat, N., & Ningsih, W. (2024). Digital Inequality: E-learning Outcomes among Youth in Indonesia. Journal Transnational Universal Studies, 2(1), 8-18. https://doi.org/10.58631/jtus.v2i1.74
186
Hosseini, D. Digital Literacy in Early Elementary School.. https://doi.org/10.31979/etd.84kt-jyz2
187
Jethani, S. and Fordyce, R. (2021). Darkness, Datafication, and Provenance as an Illuminating Methodology. M/C Journal, 24(2). https://doi.org/10.5204/mcj.2758
188
Leaver, T. (2013). The Social Media Contradiction: Data Mining and Digital Death. M/C Journal, 16(2). https://doi.org/10.5204/mcj.625
189
Liu, Z. (2025). Internet Use and Perceptions of Social Fairness in China: The Mediating Role of Social Trust and Urban-Rural Heterogeneity. Sage Open, 15(4). https://doi.org/10.1177/21582440251406692
190
Lutz, C. (2019). Digital inequalities in the age of artificial intelligence and big data. Human Behavior and Emerging Technologies, 1(2), 141-148. https://doi.org/10.1002/hbe2.140
191
Marciano, L., Vocaj, E., Bekalu, M., Tona, A., Rocchi, G., & Viswanath, K. (2023). The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review. Journal of Medical Internet Research, 25, e45540. https://doi.org/10.2196/45540
192
Oluwatimilehin, J., Evans, N., Singh, U., & Leung, W. (2021). Conceptualising digital capital in higher education institutions, its value during Covid 19 pandemic and beyond. Inkanyiso, 13(1), 9. https://doi.org/10.4102/ink.v13i1.24
193
Pilav-Velić, A., Černe, M., Trkman, P., Wong, S., & Abaz, A. (2021). Digital or Innovative: understanding “Digital Literacy – Practice – Innovative Work Behavior” Chain. South East European Journal of Economics and Business, 16(1), 107-119. https://doi.org/10.2478/jeb-2021-0009
194
Ritchie, H. (2022). An institutional perspective to bridging the divide: The case of Somali women refugees fostering digital inclusion in the volatile context of urban Kenya. New Media & Society, 24(2), 345-364. https://doi.org/10.1177/14614448211063186
195
Seaton, C., Rush, K., Li, E., Hasan, K., & Fawcus, L. (2023). Gluu Essentials Digital Skills Training for Middle-Aged and Older Adults That Makes Skills Stick: Results of a Pre-Post Intervention Study. Jmir Aging, 6, e50345. https://doi.org/10.2196/50345
196
Sáez-Linero, C. and Morales, M. (2025). Young, lower-class, and algorithmically persuaded: exploring personalized advertising and its impact on social inequality. Communication & Society. https://doi.org/10.15581/003.38.2.005
197
Wecker, C., Kohnle, C., & Fischer, F. (2007). Computer literacy and inquiry learning: when geeks learn less. Journal of Computer Assisted Learning, 23(2), 133-144. https://doi.org/10.1111/j.1365-2729.2006.00218.x
198
Welford, J., Sandhu, J., Collinson, B., & Blatchford, S. (2022). Collecting qualitative data using a smartphone app: Learning from research involving people with experience of multiple disadvantage. Methodological Innovations, 15(3), 193-206. https://doi.org/10.1177/20597991221114570
199
Giordano, A., Schmit, M., & McCall, J. (2022). Exploring adolescent social media and internet gaming addiction: The role of emotion regulation. Journal of Addictions & Offender Counseling, 44(1), 69-80. https://doi.org/10.1002/jaoc.12116
200
Oyewuwo-Gassikia, O. and Walton, Q. (2023). “We Can Only Go So Far”: Employing Intersectionality in Research with Middle-Class Black Women and Black Muslim Women. Affilia, 38(4), 656-672. https://doi.org/10.1177/08861099231196565
201
Zhou, Y. and Peterson, Z. (2024). Women’s Experiences of Sexual Harassment in Online Gaming. Violence Against Women, 31(8), 2037-2052. https://doi.org/10.1177/10778012241252021
202
Amadori, A., Real, A., Brighi, A., & Russell, S. (2025). An Intersectional Perspective on Cyberbullying: Victimization Experiences Among Marginalized Youth. Journal of Adolescence, 97(4), 931-940. https://doi.org/10.1002/jad.12466
203
Bansal, V., Rezwan, M., Iyer, M., Leasure, E., Roth, C., Pal, P., … & Hinson, L. (2023). A Scoping Review of Technology-Facilitated Gender-Based Violence in Low- and Middle-Income Countries Across Asia. Trauma Violence & Abuse, 25(1), 463-475. https://doi.org/10.1177/15248380231154614
204
Barefoot, K., Rickard, A., Smalley, K., & Warren, J. (2015). Rural lesbians: Unique challenges and implications for mental health providers.. Rural Mental Health, 39(1), 22-33. https://doi.org/10.1037/rmh0000014
205
Charmaraman, L., Hernandez, J., & Hodes, R. (2022). Marginalized and Understudied Populations Using Digital Media., 188-214. https://doi.org/10.1017/9781108976237.011
206
Dubey, A., Sinha, A., & Raj, A. (2024). Navigating the Digital Divide in India: A Comprehensive Guide. Humanities & Social Sciences Reviews, 12(2), 16-24. https://doi.org/10.18510/hssr.2024.1223
207
Elliott, K., Stacciarini, J., Jimenez, I., Rangel, A., & Fanfan, D. (2021). A Review of Psychosocial Protective and Risk Factors for the Mental Well-Being of Rural LGBTQ+ Adolescents. Youth & Society, 54(2), 312-341. https://doi.org/10.1177/0044118×211035944
208
Hong, J., Valido, A., Espelage, D., Lee, J., & DiNitto, D. (2024). Racial/ethnic differences in the bullying victimization‐suicidality link among LGBQ high school students in the United States. American Journal of Community Psychology, 74(1-2), 31-47. https://doi.org/10.1002/ajcp.12739
209
Jones, L., Nadkarni, R., & Krupa, N. (2025). Associations between county-level suicidality and cyberbullying in rural New York State youth are significantly mediated by sadness and self-harming: YRBS 2016-2023.. https://doi.org/10.1101/2025.08.27.25334601
210
Kiing, J., Ragen, E., Sulaiman, M., Goh, W., Tan, N., Ng, S., … & Loh, V. (2025). Bullying and depression among adolescents in East Asia: a scoping review on prevalence rates, risk and protective factors. Frontiers in Psychiatry, 16. https://doi.org/10.3389/fpsyt.2025.1497866
211
Kunwar, S., Sharma, S., Marasini, S., Joshi, A., Adhikari, A., Ranjit, A., … & Karmacharya, B. (2024). Cyberbullying and cyber-victimisation among higher secondary school adolescents in an urban city of Nepal: a cross-sectional study. BMJ Open, 14(3), e081016. https://doi.org/10.1136/bmjopen-2023-081016
212
Llorent, V., Ruiz, R., & Zych, I. (2016). Bullying and Cyberbullying in Minorities: Are They More Vulnerable than the Majority Group?. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01507
213
Mavhandu‐Mudzusi, A. (2022). Inclusiveness of teaching and learning to LGBTIQ individuals during COVID-19 and beyond. E-Journal of Humanities Arts and Social Sciences, 277-292. https://doi.org/10.38159/ehass.2022sp31122
214
Ortiz-Marcos, J., Fernández, M., & Fernández-Leyva, C. (2021). Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions. Future Internet, 13(1), 15. https://doi.org/10.3390/fi13010015
215
Osborne, A. (2025). Balancing the benefits and risks of social media on adolescent mental health in a post-pandemic world. Child and Adolescent Psychiatry and Mental Health, 19(1). https://doi.org/10.1186/s13034-025-00951-z
216
Trapero, F., Parra, J., & González-Martínez, M. (2021). Teachers’ perceptions of ICT issues in education: an approximation by gender and region in Mexico. On the Horizon the International Journal of Learning Futures, 29(3), 101-116. https://doi.org/10.1108/oth-03-2021-0047
217
Vogel, E., Nhem, M., Das, M., & Romm, K. (2025). Social media use and health outcomes as moderated by rurality among sexual minority young adults. American Journal on Addictions. https://doi.org/10.1111/ajad.70086
218
Walker, R., Usher, K., Jackson, D., Reid, C., Hopkins, K., Shepherd, C., … & Marriott, R. (2021). Connection to… Addressing Digital Inequities in Supporting the Well-Being of Young Indigenous Australians in the Wake of COVID-19. International Journal of Environmental Research and Public Health, 18(4), 2141. https://doi.org/10.3390/ijerph18042141
219
Zhou, S. (2021). Status and Risk Factors of Chinese Teenagers’ Exposure to Cyberbullying. Sage Open, 11(4). https://doi.org/10.1177/21582440211056626
220
Akpınar, Y. and Aslan, Ü. (2015). Supporting Children’s Learning of Probability Through Video Game Programming. Journal of Educational Computing Research, 53(2), 228-259. https://doi.org/10.1177/0735633115598492
221
Alhasan, K., Alhasan, K., & Hashimi, S. (2023). Roblox in Higher Education. International Journal of Emerging Technologies in Learning (Ijet), 18(19), 32-46. https://doi.org/10.3991/ijet.v18i19.43133
222
Boulton, H., Spieler, B., Petri, A., Schindler, C., Slany, W., & Beltran, X. (2016). THE ROLE OF GAME JAMS IN DEVELOPING INFORMAL LEARNING OF COMPUTATIONAL THINKING: A CROSS-EUROPEAN CASE STUDY.. https://doi.org/10.21125/edulearn.2016.0538
223
Elo, J., Lumivalo, J., & Tuunanen, T. (2022). A Personal Values-Based Approach to Understanding Users’ Co-Creative and Co-Destructive Gaming Experiences in Augmented Reality Mobile Games. Pacific Asia Journal of the Association for Information Systems, 14, 51-81. https://doi.org/10.17705/1pais.14503
224
Luan, C. and Phan, T. (2023). Immersive technology and cause‐related marketing: The role of personalization and value co‐creation. Journal of Consumer Behaviour, 23(3), 1574-1596. https://doi.org/10.1002/cb.2293
225
Othman, M., Jazlan, S., Yamin, F., Aman, S., Mohamad, F., Anuar, N., … & Manaf, A. (2022). Mapping Computational Thinking Skills Through Digital Games Co-Creation Activity Amongst Malaysian Sub-urban Children. Journal of Educational Computing Research, 61(2), 355-389. https://doi.org/10.1177/07356331221121106
226
Pacheco-Velázquez, E., Rodés, V., Rabago-Mayer, L., & Bester, A. (2023). How to Create Serious Games? Proposal for a Participatory Methodology. International Journal of Serious Games, 10(4), 55-73. https://doi.org/10.17083/ijsg.v10i4.642
227
Roberts-Woychesin, J. Understanding 3-D Spaces Through Game-based Learning: a Case Study of Knowledge Acquisition Through Problem-based Learning in Minecraft.. https://doi.org/10.12794/metadc804920
228
Thomas, A. (2023). Merit and monetisation: A study of video game user-generated content policies. Internet Policy Review, 12(1). https://doi.org/10.14763/2023.1.1689
229
Zhang, S. (2025). UGC user-generated content (UGC): Digital laborers’ immaterial labor in virtual cultural spaces. The ‘Eggy Party’ online game as a Chinese media phenomenon. Thesis Eleven, 190(1), 82-94. https://doi.org/10.1177/07255136251372659
230
Anand, N., Sharma, M., Thakur, P., Mondal, I., Sahu, M., Singh, P., … & Singh, R. (2021). Doomsurfing and doomscrolling mediate psychological distress in COVID‐19 lockdown: Implications for awareness of cognitive biases. Perspectives in Psychiatric Care, 58(1), 170-172. https://doi.org/10.1111/ppc.12803
231
Blacha, S., Anderson, I., & Mar, R. (2025). Tension in attention: Hypervigilance helps explain why marginalization leads to doomscrolling.. Psychology of Popular Media, 14(4), 550-559. https://doi.org/10.1037/ppm0000575
232
Güme, S. (2024). Doomscrolling: A Review. Psikiyatride Guncel Yaklasimlar – Current Approaches in Psychiatry, 16(4), 595-603. https://doi.org/10.18863/pgy.1416316
233
Hou, C., Foscht, T., Duffek, B., Arigayota, A., & Eisingerich, A. (2025). Mitigating the Negative Effects of Internet Browsing on Young People’s Resilience and Outlook on Life Through Classic Grimms’ Fairy Tales: Exploratory Randomized Controlled Study. Jmir Formative Research, 9, e76770. https://doi.org/10.2196/76770
234
Kang, H. and Lou, C. (2022). AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement. Journal of Computer-Mediated Communication, 27(5). https://doi.org/10.1093/jcmc/zmac014
235
Kar, S., Dube, R., Goud, M., Gibrata, Q., El-Balbissi, A., Salim, T., … & Fatayerji, R. (2025). Impact of Screen Time on Development of Children. Children, 12(10), 1297. https://doi.org/10.3390/children12101297
236
Langlais, M., Thaler, A., & West, E. (2024). TikTok Too Much? A Qualitative Investigation of Adolescent TikTok Use, Motivation, and Consequences. Youth & Society, 57(4), 593-614. https://doi.org/10.1177/0044118×241282347
237
McKelvey, F. and Hunt, R. (2019). Discoverability: Toward a Definition of Content Discovery Through Platforms. Social Media + Society, 5(1). https://doi.org/10.1177/2056305118819188
238
Pilipets, E. (2019). From Netflix Streaming to Netflix and Chill: The (Dis)Connected Body of Serial Binge-Viewer. Social Media + Society, 5(4). https://doi.org/10.1177/2056305119883426
239
Poole, M., Pancer, E., Philp, M., & Noseworthy, T. (2023). COVID-19 and the decline of active social media engagement. European Journal of Marketing, 58(2), 548-571. https://doi.org/10.1108/ejm-12-2022-0927
240
Scalvini, M. (2023). Making Sense of Responsibility: A Semio-Ethic Perspective on TikTok’s Algorithmic Pluralism. Social Media + Society, 9(2). https://doi.org/10.1177/20563051231180625
241
Sharma, B., Lee, S., & Johnson, B. (2022). The dark at the end of the tunnel: Doomscrolling on social media newsfeeds.. Technology Mind and Behavior, 3(1), 144-156. https://doi.org/10.1037/tmb0000059
242
Thomas, G., Bennie, J., Cocker, K., & Biddle, S. (2020). Exploring contemporary screen time in Australian adolescents: A qualitative study. Health Promotion Journal of Australia, 32(S2), 238-247. https://doi.org/10.1002/hpja.440
243
Tuck, A. and Thompson, R. (2024). Types of social media use are differentially associated with trait and momentary affect.. Emotion, 24(7), 1600-1611. https://doi.org/10.1037/emo0001379
244
Yousef, A., Alshamy, A., Tlili, A., & Metwally, A. (2025). Demystifying the New Dilemma of Brain Rot in the Digital Era: A Review. Brain Sciences, 15(3), 283. https://doi.org/10.3390/brainsci15030283
245
Şot, İ. (2023). Scrolling TikTok to Soothe and Foster Self-Care During the COVID-19 Pandemic. Social Media + Society, 9(4). https://doi.org/10.1177/20563051231213542
246
Abdeljaber, L., Alsarra, S., Khan, L., Robinson, R., Ruano, A., & Haque, U. (2025). Integrating Retrieval-Augmented Generation and Thematic NLP for Vaccine Confidence Modeling in Alaska.. https://doi.org/10.21203/rs.3.rs-7368501/v1
247
Garaschuk, D. (2024). Digital echo chambers: amplifying populist rhetoric in the age of social media. Актуальні проблеми філософії та соціології, (46), 152-157. https://doi.org/10.32782/apfs.v046.2024.26
248
Garaschuk, D. (2024). “TRUTH DECAY” AND POPULISM: ERODING DEMOCRACY IN THE 21ST CENTURY. International and Political Studies, (37), 65-78. https://doi.org/10.32782/2707-5206.2024.37.6
249
Guo, J. and Dong, X. (2024). Book Review:
250
a. Pandemics in the Age of Social Media: Information and Misinformation in Developing Nations
251
b. , by Vikas Kumar and Mohit Rewari Pandemics in the Age of Social Media: Information and Misinformation in Developing Nations, edited by KumarVikasRewariMohit. London and New York: Routledge, 2024, 191 pp. $166.34 (hardcover). ISBN: 978-1-032-32393-0 (hbk).. Television & New Media, 26(6), 740-744. https://doi.org/10.1177/15274764241302790
252
Karmila, L., Rachmiatie, A., K, S., Fardiah, D., Ahmadi, D., & Muhtadi, A. (2024). The role of social media in the political construction of identity: Implications for political dynamics and democracy in Indonesia. Journal of Infrastructure Policy and Development, 8(14), 9171. https://doi.org/10.24294/jipd9171
253
Kısa, S. and Kısa, A. (2024). A Comprehensive Analysis of COVID-19 Misinformation, Public Health Impacts, and Communication Strategies: Scoping Review. Journal of Medical Internet Research, 26, e56931. https://doi.org/10.2196/56931
254
Masum, M. (2025). Understanding the Spread of Health Misinformation During Public Health Crises: A Cognitive and Media Effects Perspective With Evidence From COVID-19.. https://doi.org/10.21203/rs.3.rs-8098073/v1
255
Nasution, M., Safirra, H., & Farid, A. (2023). Public Perception of Environmental and Sustainability Issues in North Sumatra: A Mass Media Perspective. Jurnal Ilmu Sosial Mamangan, 12(2), 327-342. https://doi.org/10.22202/mamangan.v12i2.6847
256
Osborne, A. (2025). Bridging the Infodemic Equity Gap: North-South Digital Health Disparities and a Framework for Action. Journal of Medical Internet Research, 27, e80013-e80013. https://doi.org/10.2196/80013
257
Panjaitan, N., Sihombing, S., Palen, K., Schiavo, R., & Lipschultz, L. (2023). Enhancing Government Communication Strategies for Effective Health In-formation and Public Health Education. LawEco, 17(2), 151-169. https://doi.org/10.35335/laweco.v17i2.6
258
Philipp‐Muller, A., Lee, S., & Petty, R. (2022). Why are people antiscience, and what can we do about it?. Proceedings of the National Academy of Sciences, 119(30). https://doi.org/10.1073/pnas.2120755119
259
Singh, H. (2022). Impact of social media on interpersonal communication. International Journal of Communication and Information Technology, 3(2), 26-30. https://doi.org/10.33545/2707661x.2022.v3.i2a.69
260
Sutton, J., Rivera, Y., Sell, T., Moran, M., Gayle, D., Schoch‐Spana, M., … & Turetsky, D. (2021). Longitudinal Risk Communication: A Research Agenda for Communicating in a Pandemic. Health Security, 19(4), 370-378. https://doi.org/10.1089/hs.2020.0161
261
Swinford, D. and Zadeh, A. (2025). COVID vaccine misinformation: Toward an integrated approach for predicting the cascade of disinformation. Information Services & Use, 45(1-2), 125-139. https://doi.org/10.1177/18758789251332783
262
Young, D. (2021). Young and Miller, Political Communication in Oxford Handbook of Poli Psych 3rd ed.. https://doi.org/10.31219/osf.io/mwdtu
263
Graham, G., Ostrowski, M., & Sabina, A. (2016). Population health-based approaches to utilizing digital technology: a strategy for equity. Journal of Public Health Policy, 37(S2), 154-166. https://doi.org/10.1057/s41271-016-0012-5
264
Hampton, J., Mugambi, P., Caggiano, E., Eugene, R., Valente, A., Taylor, M., … & Carreiro, S. (2024). Closing the Digital Divide in Interventions for Substance Use Disorder. Journal of Psychiatry and Brain Science. https://doi.org/10.20900/jpbs.20240002
265
Haynes, N., Ezekwesili, A., Nunes, K., Gumbs, E., Haynes, M., & Swain, J. (2021). “Can you see my screen?” Addressing Racial and Ethnic Disparities in Telehealth. Current Cardiovascular Risk Reports, 15(12). https://doi.org/10.1007/s12170-021-00685-5
266
Keadle, S., Bustamante, E., & Buman, M. (2021). Physical Activity and Public Health: Four Decades of Progress. Kinesiology Review, 10(3), 319-330. https://doi.org/10.1123/kr.2021-0028
267
Lyles, C., Nguyen, O., Khoong, E., Aguilera, A., & Sarkar, U. (2023). Multilevel Determinants of Digital Health Equity: A Literature Synthesis to Advance the Field. Annual Review of Public Health, 44(1), 383-405. https://doi.org/10.1146/annurev-publhealth-071521-023913
268
Mintz, J. (2013). Can smartphones support inclusion for autism in mainstream?. Journal of Assistive Technologies, 7(4), 235-242. https://doi.org/10.1108/jat-08-2013-0019
269
Oudat, Q., Messiah, S., & Ghoneum, A. (2025). A Multi-Level Approach to Childhood Obesity Prevention and Management: Lessons from Japan and the United States. Nutrients, 17(5), 838. https://doi.org/10.3390/nu17050838
270
Owen, D. and Sanchez, C. (2020). Cell Phones as Instructional Technology in the Civics Classroom.. https://doi.org/10.33774/apsa-2020-cph32
271
Partridge, S., Knight, A., Todd, A., McGill, B., Wardak, S., Alston, L., … & Raeside, R. (2024). Addressing disparities: A systematic review of digital health equity for adolescent obesity prevention and management interventions. Obesity Reviews, 25(12). https://doi.org/10.1111/obr.13821
272
Rangachari, P., Arkoubi, K., & Shindi, R. (2025). A multi-level framework for advancing digital health equity in learning health systems: aligning practice and theory with the Quintuple Aim. International Journal for Equity in Health, 24(1). https://doi.org/10.1186/s12939-025-02663-4
273
Yan, C., Hughes, M., Crawford, L., Cantu-Hines, M., George, R., Dev, D., … & Bashford, G. (2025). Health equity in Nebraska: addressing disparities through place-based policy innovation. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1651092
274
(2023). The state of implementation of the OECD AI Principles four years on.. https://doi.org/10.1787/835641c9-en
275
Anderson, B. and Sutherland, E. (2024). Collective action for responsible AI in health.. https://doi.org/10.1787/f2050177-en
276
Azer, M. and Samir, R. (2024). Overview of the Complex Landscape and Future Directions of Ethics in Light of Emerging Technologies. International Journal of Advanced Computer Science and Applications, 15(7). https://doi.org/10.14569/ijacsa.2024.01507142
277
Bell, A., Nov, O., & Stoyanovich, J. (2023). Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance. Data & Policy, 5. https://doi.org/10.1017/dap.2023.8
278
Braga, J., Stiubiener, I., Henriques, P., & Cananea, H. (2024). How multistakeholder governance can shape reponsible AI.. https://doi.org/10.31219/osf.io/kdw52
279
Chang, W., Liao, Y., Chao, E., Liu, S., & Lee, T. (2024). Ethical concerns about artificial intelligence: Evidence from a national survey in Taiwan.. https://doi.org/10.21203/rs.3.rs-3765278/v1
280
Chauhan, A. (2025). Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges. BMC Medical Ethics, 26(1). https://doi.org/10.1186/s12910-025-01265-7
281
Duguet, A. (2022). Place du numérique et de l’intelligence artificielle dans l’activité médicale. Éléments pour une réflexion commune entre médecins et entreprises., 403-417. https://doi.org/10.4000/books.putc.15470
282
Floridi, L. and Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1
283
Gardner, A., Smith, A., Steventon, A., Coughlan, E., & Oldfield, M. (2021). Ethical funding for trustworthy AI: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy AI practice. Ai and Ethics, 2(2), 277-291. https://doi.org/10.1007/s43681-021-00069-w
284
Henriques, I. and Hartung, P. (2021). Children’s Rights by Design in AI Development for Education. The International Review of Information Ethics, 29. https://doi.org/10.29173/irie424
285
Lorenzo, M. and Martínez-Carrera, S. (2021). Digital Newspapers’ Perspectives about Adolescents’ Smartphone Use. Sustainability, 13(9), 5316. https://doi.org/10.3390/su13095316
286
Machado, H., Silva, S., & Neiva, L. (2023). Publics’ views on ethical challenges of artificial intelligence: a scoping review. Ai and Ethics, 5(1), 139-167. https://doi.org/10.1007/s43681-023-00387-1
287
Radanliev, P. and Santos, O. (2023). Ethics and Responsible AI Deployment.. https://doi.org/10.33774/apsa-2023-f1fkq
288
Robles, P. and Mallinson, D. (2023). Catching up withAI: Pushing toward a cohesive governance framework. Politics & Policy, 51(3), 355-372. https://doi.org/10.1111/polp.12529
289
Shackelford, S., Asare, I., Dockery, R., Raymond, A., & Sergueeva, A. (2021). Should We Trust a Black Box to Safeguard Human Rights? a Comparative Analysis of Ai Governance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3773198
290
Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. (2024). Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review. Jmir Medical Informatics, 12, e50048. https://doi.org/10.2196/50048
291
Taumoepeau, E. (2022). An Ethical Framework for Facial Recognition Use in New Zealand. Proceedings of the Wellington Faculty of Engineering Ethics and Sustainability Symposium. https://doi.org/10.26686/wfeess.vi.7663
292
Yan, Y., Liu, H., & Chau, T. (2025). A Systematic Review of AI Ethics in Education. Journal of Global Information Management, 33(1), 1-50. https://doi.org/10.4018/jgim.386381
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