We surveyed 1,257 12- to 18-year-old adolescents attending 52 schools in urban or suburban areas of Argentina. We recorded the daily exposure to various screen-based activities, including video- and online-gaming, social media, TV or streaming. Screen time and device type in the hour before bedtime, sleep patterns during weekdays and weekends, somnolence (Pediatric Daytime Sleepiness Scale score), and grades in language and mathematics were also assessed.

This is a cross-sectional dataset of US adolescents, collected in Spring 2021. Participants completed measures of school context, satisfaction with school, social connection, media use, and mental health.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

This project examined the role of technology use in teen dating violence and abuse, and bullying. The goal of the project was to expand knowledge about the types of abuse experiences youth have, the extent of victimization and perpetration via technology and new media (e.g., social networking sites, texting on cellular phones), and how the experience of such cyber abuse within teen dating relationships or through bullying relates to other life factors. This project carried out a multi-state study of teen dating violence and abuse, and bullying, the main component of which included a survey of youth from ten schools in five school districts in New Jersey, New York, and Pennsylvania, gathering information from 5,647 youth about their experiences. The study employed a cross-sectional, survey research design, collecting data via a paper-pencil survey. The survey targeted all youth who attended school on a single day and achieved an 84 percent response rate.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

BackgroundSeveral large-scale studies and reviews have reported both negative and positive associations of social media use with well-being, suggesting that the findings are more complex and need more nuanced study. Moreover, there is little or no exploration of how social media use in adolescence influences flourishing, a more all-encompassing construct beyond well-being, including six sub-domains (i.e., happiness, meaning and purpose, physical and mental health, character, close social relationships, and financial stability). This paper aims to fill this gap by understanding how adolescents might flourish through social media activities by fulfilling the basic needs pointed out by the Self-Determination Theory, i.e., relatedness, autonomy, and competence.MethodsThe study is drawn on cross-sectional data collected from 1,429 Swiss adolescents (58.8% females, Mage = 15.84, SDage = 0.83) as part of the HappyB project in Spring 2022. Self-reported measures included the Harvard Adolescent Flourishing scale, positive and negative online social experiences, self-disclosure on social media, and social media inspiration. Control variables included, among others, self-esteem, ill-being, and personality.ResultsAfter applying Bonferroni’s correction, results of the hierarchical regression analyses showed that positive social media experiences (β = 0.112, p < 0.001) and social media inspirations from others (β = 0.072, p < 0.001) and for others (β = 0.060, p = 0.003) were positively associated with flourishing. Flourishing was inversely associated with negative social media experiences (β = −0.076, p < 0.001). Among covariates, self-esteem (β = 0.350, p < 0.001), ill-being (β = −0.252, p < 0.001), perceived school environment (β = 0.138, p < 0.001), self-reported level of physical activity (β =0.109, p < 0.001), and perceived socio-economic status (β = −0.059, p = 0.001) were all related to flourishing. In contrast, gender, high school year, age, perceived stress, and personality (extraversion and neuroticism) were not.ConclusionUsing a well-being framework to investigate social media use in adolescents is needed to go beyond the ill-being perspective. Our results align with the needs pointed out by the Self-Determination Theory. Carrying out social media activities in a way that promotes—rather than diminishes—flourishing should be included as an additional good habit influencing adolescents’ development. We suggest that interventions aiming to foster adolescents’ flourishing should include curricula aiming to promote a good use of social media through positive online social relationships and inspirational contents.

This proof of concept study harnesses novel transdisciplinary insights to contrast two school-based smoking prevention interventions among adolescents in the UK and Colombia. We compare schools in these locations because smoking rates and norms are different, in order to better understand social norms based mechanisms of action related to smoking. We aim to: (1) improve the measurement of social norms for smoking behaviors in adolescents and reveal how they spread in schools; (2) to better characterize the mechanisms of action of smoking prevention interventions in schools, learning lessons for future intervention research. The A Stop Smoking in Schools Trial (ASSIST) intervention harnesses peer influence, while the Dead Cool intervention uses classroom pedagogy. Both interventions were originally developed in the UK but culturally adapted for a Colombian setting. In a before and after design, we will obtain psychosocial, friendship, and behavioral data (e.g., attitudes and intentions toward smoking and vaping) from ~300 students in three schools for each intervention in the UK and the same number in Colombia (i.e., ~1,200 participants in total). Pre-intervention, participants take part in a Rule Following task, and in Coordination Games that allow us to assess their judgments about the social appropriateness of a range of smoking-related and unrelated behaviors, and elicit individual sensitivity to social norms. After the interventions, these behavioral economic experiments are repeated, so we can assess how social norms related to smoking have changed, how sensitivity to classroom and school year group norms have changed and how individual changes are related to changes among friends. This Game Theoretic approach allows us to estimate proxies for norms and norm sensitivity parameters and to test for the influence of individual student attributes and their social networks within a Markov Chain Monte Carlo modeling framework. We identify hypothesized mechanisms by triangulating results with qualitative data from participants. The MECHANISMS study is innovative in the interplay of Game Theory and longitudinal social network analytical approaches, and in its transdisciplinary research approach. This study will help us to better understand the mechanisms of smoking prevention interventions in high and middle income settings.

Problematic Internet use (PIU) in adolescents is a global public health issue especially exacerbated by the restrictions caused by the COVID-19 epidemic. The authors assume a negative impact of PIU not only in relation to adolescents’ mental health and social behaviour but also relative to somatic components of their health, particularly concerning the occurrence of pain symptoms – recurrent headache (RH), recurrent abdominal pain (RAP), and recurrent back pain (RBP). The present study aims to identify the associations between different types of PIU (generalized PIU – PIUgen, problematic computer game use – PUgame, and problematic social media use – PUsocial), and pain symptoms (RH, RAP and RBP), and to determine the role of psychosocial factors in the emergence of these associations. Methods: The research represents a one-stage observational study of 4,411 adolescents (53.6% girls; Mage = 14.53±1.52) in an unbiased school sample, held in three large cities of Central Siberia. The frequ..., Participants The study is a one-stage observational survey of an unbiased school sample in three large cities in Central Siberia. The research object is represented by 12-18-year-old adolescents (n = 4,411) – students of 10 comprehensive schools in Krasnoyarsk, Russia (n = 2,843), 4 comprehensive schools in Abakan, Russia (n = 1,357) and 2 comprehensive schools in Kyzyl, Russia (n = 211). Measurement After obtaining informed consent from the parents and confirming the voluntariness of participation with the students, the researchers assured the latter of the confidentiality of the study. The students were asked to complete paper versions of self-report questionnaires within 45 minutes in a classroom setting. The survey was conducted in the spring of 2019. The study was approved by the Ethics Committee of the Federal Research Centre “Krasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Sciences†. Recurrent pain symptoms measurement Adolescents were asked a number o..., , # Recurrent pain symptoms in adolescents with generalized and specific problematic Internet use: Associations analysis, confounding and mediating effects of comorbid psychosocial problems

https://doi.org/10.5061/dryad.1g1jwsv4z

This dataset contains primary data from a survey of 4411 adolescents. The dataset contains demographic data, test data from the following questionnaires:

 - The Strengths and Difficulties Questionnaire (SDQ)

- The Chen Internet Addiction Scale (CIAS)

- The Game Addiction Scale for Adolescents (GASA)

- The Social Media Disorder Scale (SMDS)

In addition, the dataset contains coded responses from adolescents regarding the frequency and intensity of recurrent pain symptoms: headache, abdominal pain, and back pain.

Description of the data

Code: the unique code of the adolescent

Age_group:Â 1 - <= 14 years; 2 > 14 years

Sex: 1 - male; 2 - female

Ethnicity: Russians - 1; Tuvans - 2; Khakass - 3; Others - ...

The file describes the data of the SNARE project (Social Networks and Risk Behavior in Early Adolescence). This was a longitudinal research project on the social development of early adolescents with a specific focus on adolescents’ peer relationships and their involvement in risky behavior, including both self-reported data and peer-nomination data. All first-year and second-year students in two secondary schools in the Netherlands were approached to take part in the project (Cohort 1) at the beginning of the academic year 2011–2012. A second cohort of students entering their first year in these secondary schools was asked to take part in the project the following academic year 2012–2013 (Cohort 2). Data were collected three times in one academic year (in the fall, winter, and spring), starting in 2011–2012 (Cohort 1) and 2012–2013 (Cohort 2), respectively. In total, 12 waves of data have been collected. Before data collection started, students received an information letter describing the goal of the study and offering the possibility to refrain from participation. Parents who did not wish their children to participate in the study were asked to indicate this and students were made aware that they could cease their participation at any time. The survey was completed in the classroom by computer, supervised by a researcher, using Bright Answer socio software (SNARE software 2011). The privacy and anonymity of the students were warranted, and the study was approved by the Internal Review Board (IRB) of Utrecht University (see also Franken et al. 2016; the project name is “Social Network Processes and Social Development of Children and Adolescents”).

ABSTRACT Objectives: To identify scientific evidence on the influence of internet use on adolescents’ biopsychosocial health. Methods: Integrative review, with database search, using the descriptors “internet”, “adolescent health” and “adolescent behavior”. After applying the inclusion and exclusion criteria, 16 articles were selected. Results: Knowledge convergence produced for three main themes was demonstrated: “Internet exposure time and possible damages to adolescent health”; “Internet, adolescent and cyberbullying”; and “Internet as a source of information for adolescent health”. Final considerations: The network involves an intricate network of interactions, providing varied behaviors and attitudes that reflect on adolescent health. Therefore, it is important to articulate nursing actions with the school community and the family, in order to carry out health education.

This resource is a compilation of select training and technical assistance (TTA) centers that are funded by the U.S. Department of Health and Human Services (HHS) and offer resources, training, and intensive technical assistance focused on supporting the behavioral health of children and youth.

This document is intended as a technical assistance resource to support navigation to existing HHS-funded resources and supports and does not constitute federal policy or guidance. Readers are encouraged to consult agency language and TTA center websites for more information. Although not all HHS-funded TTA centers with expertise and content focused on children and youth behavioral health are included in this document, it is intended to highlight the types of TTA that HHS is supporting in this area.

Description: Develops and disseminates information, guidance, and training on the impact of children and youth’s social media use (risks and benefits), especially the potential risks social media platforms pose to their mental health and the clinical and societal interventions that could be used to address these risks.

Supporting agency: Substance Abuse and Mental Health Services Administration

Website: Center of Excellence on Social Media and Youth Mental Health

Description: Works with state and jurisdiction Maternal & Child Health and Injury & Violence prevention programs, including suicide and self-harm prevention, to create an environment in which all infants, children, and youth are safe and healthy.

Supporting agency: Health Resources and Services Administration

Website: Children's Safety Network

Description: Provides technical assistance to EMSC State Partnership grantees. This includes providing technical assistance to emergency departments that are increasingly serving as the front door to care for children in crisis.

Supporting agency: Health Resources and Services Administration

Website: EMSC Innovation and Improvement Center

Description: Provides comprehensive and intensive TTA to recipients of two grant programs funded by the Substance Abuse and Mental Health Services Administration: Infant and Early Childhood Mental Health grants and Project LAUNCH (Linking Actions for the Unmet Needs in Children’s Health) grants.

Supporting agency: Substance Abuse and Mental Health Services Administration

Website: Infant and Early Childhood Mental Health (IECMH) Training and Technical Assistance Center

Description: The Medicaid School-Based Services Technical Assistance Center:

Supporting agencies: Centers for Medicare & Medicaid Services, U.S. Department of Education

Website: Medicaid School-Based Services Technical Assistance Center

Description: Provides support to states, tribes, and territories to build bridges between child welfare systems and state mental health systems to bring about systemic change to improve the mental health outcomes for children and families impacted by the child welfare system.

Supporting agency: Administration for Children and Families

Website: National Center for Adoption Competent Mental Health Services

Description: Supports state and community leaders and their partners in the planning and implementation of rigorous approaches to quality in all early care and education settings for children from birth to school age. The Center works across early childhood sectors, including child care and Head Start, to address the following priorities:

Supporting agency: Administration for Children and Families

Website: National Center on Early Childhood Quality Assurance

Description: Designs evidence-based resources and delivers innovative training and technical assistance to build the capacity of Head Start and other early childhood programs to:

Supporting agencies: Administration for Children and Families [HC(1]

Website: National Center on Health, Behavioral Health, and Safety

Description: Provides national TTA to child welfare, dependency court, and substance use treatment professionals to improve

The Student Nutrition Program helps provide healthy breakfasts, snacks and lunches to school-age kids across Ontario. This dataset contains a list of Student Nutrition Program sites at schools and community locations. The dataset contains: * school name * Ministry of Education school ID * school board * school address * program type (breakfast/morning meal, lunch, or snack) Where an address is not provided, the school or community location does not have a Ministry of Education school ID number. Some entries may appear to be duplicates but represent two separate programs in one school or community location. *[ID]: identification

The dataset includes information about gaming activity, potential gaming, Internet and SNS addiction, collected among a large sample of Polish adolescents. The study was a part of the project named: "Internet Gaming Disorder - the characteristics and prevalence of the phenomenon and its psychological correlates among primary and lower secondary school students in the Kuyavian-Pomeranian and Warmian-Masurian voivodships" - a public task co-financed by the Gambling Problem Solving Fund at the disposal of the Minister of Health (Poland), grant number 165/HBK/2018. Data were collected in 2018 in two voivodships: kujawsko-pomorskie (Kuyavian-Pomeranian) and warmińsko-mazurskie (Warmian-Masurian). The selection of educational institutions (primary and lower secondary schools) was random. The study was conducted in 10 locations (urban and rural). The sample consists of 1500 students aged 10-18 years. Headmaster of the particular school gave the consent for the study. The students during school time (usually at educational lesson) were informed about the purpose of the study, that it is anonymous and participation is voluntary, and that they may refuse or resign from it at any time. Dataset contains sociodemographic variables. The following measures were used: (i) Gaming activity questions (playing or not playing; types of games played - online, offline; with who the participant play games; devices used for gaming, frequency, emotions related to gaming, parents control and interest in children gaming, parents gaming activity); (ii) The Internet Gaming Disorder Scale-Short Form (IGD9-SF; Pontes & Griffiths, 2015); (iii) Ten-Item Internet Gaming Disorder Test (IGDT-10; Király, Sleczka, et al., 2017); (iv) Internet Addiction Test (IAT; Young 1998),(v) The Scale of Excessive Use of Social Networking Sites (Kotyśko, Michalak, in press). Measures of Internet Gaming Disorder (IGDS9-SF and IGDT-10) were randomly distributed to participants (one half of the measure sets included IGDS9-SF and other half the IGDT-10 - however all of the sets were mixed to improve the randomisation). The rest of the measures were used among all participants. Date: 2018-02-01 – 2018-06-21 (data collection)

IntroductionSince the Coronavirus disease 2019 (COVID-19), public safety measures, including social distancing and school closures, have been implemented, precipitating psychological difficulties and heightened online activities for adolescents. However, studies examining the impact of the pandemic on adolescent mental health and their coping strategies in Asian countries are limited. Further, most studies have used survey measures to capture mental health challenges so far. Accordingly, this study aimed to examine the psychological challenges South Korean adolescents experienced and their coping strategies during the pandemic using the Natural Language Processing (NLP) and Text mining (TM) technique on adolescents’ social media texts/posts.MethodsThe data were gathered from social media texts/posts such as online communities, Twitter, and personal blogs from January 1, 2019, to October 31, 2021. The 12,520,250 texts containing keywords related to adolescents’ common psychological difficulties reported during the pandemic, including self-harm, Attention-Deficit/Hyperactivity Disorders (ADHD), and insomnia, were analyzed by TM, NLP using information extraction, co-occurrence and sentiment analysis. The monthly frequency of the keywords and their associated words was also analyzed to understand the time trend.ResultsAdolescents used the word “self-harm” in their social media texts more frequently during the second wave of COVID-19 (August to September 2020). “Friends” was the most associated word with “self-harm.” While the frequency of texts with “Insomnia” stayed constant throughout the pandemic, the word “ADHD” was increasingly mentioned in social media. ADHD and insomnia were most frequently associated with ADHD medications and sleeping pills, respectively. Friends were generally associated with positive words, while parents were associated with negative words.ConclusionDuring COVID-19, Korean adolescents often expressed their psychological challenges on social media platforms. However, their coping strategies seemed less efficient to help with their difficulties, warranting strategies to support them in the prolonged pandemic era. For example, Korean adolescents shared psychological challenges such as self-harm with friends rather than their parents. They considered using medicine (e.g., sleeping pills and ADHD medication) as coping strategies for sleep and attention problems.

Overview

Objectives of Global Youth Tobacco Survey:

To determine the level of tobacco use by State/UTs, sex, location of school (urban/rural). To estimate the age of initiation of cigarette and bidi smoking and smokeless tobacco. To estimate the exposure to secondhand smoking (SHS). To estimate the exposure to tobacco advertising

Available Columns in the dataset

1. Use of any form of tobacco, i.e. smoking, smokeless, and any other form of tobacco products;
2. Ever tried or experimented any form of tobacco even once;
3. Use of any form of tobacco in past 30 days;
4. Includes other form of smoking products in addition to cigarette and bidi such as hookah, cigars, cheroots, cigarillos, water pipe, chillum, chutta, dhumti,
5. Use of paan masala together with tobacco was asked directly as one of the categories of smokeless tobacco;
6. Susceptibility to future cigarette use includes those who answered Yes, or maybe to using tobacco products if one of their best friends offered it to them;
7. E-cigarette is part of Electronic Nicotine Delivery System (ENDS) and includes like devices and other emerging products;
8. Stopped using tobacco in past 12 months;
9. Refers to current tobacco users only;
10. Secondhand smoking or passive smoking refers to exposure to other peoples smoking in past 7 days;
11. Refers to schools, hostels, shops, restaurants, movie theatres, public conveyances, gyms, sports arenas, airports, auditorium, hospital building, railway waiting room, public toilets, public offices, educational institutions, libraries, etc.; 12. Refers to playgrounds, sidewalks, entrances to buildings, parks, beaches, bus stops, market places, etc.; #. the value 0.0 represent prevalence of less than 0.05.
12. Refers to source of obtaining tobacco products by current users at the time of last use in past 30 days and the two major sources are given here, therefore, these two figures may not add upto 100% as there are other sources;
13. Includes any form of mass media, fairs, concerts, sporting, community events or social gatherings, tobacco products packages and taught in class;
14. Mass media includes television, radio, internet, billboards, posters, newspapers, magazines, movies, etc.;
15. Social events include sports events, fairs, concerts, community events, social gatherings etc.;
16. Includes any form of media or point of sale;
17. Point of Sale includes any stores, grocery shops, paan shops etc.; 19.Unit of analysis is the school (unweighted);
18. Cigarettes and Other Tobacco Products (Prohibition of Advertisement and Regulation of Trade and Commerce, Production, Supply and Distribution) Act, 2003.

License: Brief of Open Government Data (OGD) License- India

ABSTRACT Objective: To describe the social network of adolescents who need special health care. Method: A qualitative, descriptive and exploratory study conducted between 2016 and 2017 in the pediatric outpatient clinic of a teaching hospital in southern Brazil. Thirty-five semi-structured interviews were conducted with adolescents between 12 and 18 years of age, followed by the construction of genograms and ecomaps. After transcription, the enunciations were subjected to Pêcheux’s method of discourse analysis. Results: The institutional network consists of health services, schools and religious entities, as well as adolescents’ families and friends. In the family network, women family members—such as mothers, grandmothers and aunts—have a special role. Final Consideration: The adolescents’ social network is composed of institutional and family circles. In the view of adolescents, the tertiary service is more capable of solving their problems, and for this reason used the most. In order to ensure these adolescents access to and continuity of care, the articulation between health policies and health services is suggested.

The study is a panel survey of adolescents designed to study the complex causal mechanism of structural, social, and cultural integration of adolescents with migration background. The data of three waves are currently available.

The data set of the first wave includes surveys of students, parents, and teachers. It enables studying processes of intergenerational transmission and integration. The survey covers topics of (1) cognitive-cultural integration), (2) structural integration, (3) social integration, (4) emotional-cultural integration, and (5) health and wellbeing. In addition there is (6) detailed information about migration experience and demographics.

Furthermore, the cognitive-cultural integration on the basis of (1) language proficiency tests (measuring linguistic skills) and (2) a cognitive skills test (measurement of intelligence) was measured.

In addition, two aspects of social integration were measured: (1) social integration outside the class context by means of egocentric networks and (2) social integration within the class context by means of a sociometric questionnaire.

The data set of the second wave includes re-interviews with students from the first wave. In addition, in the Netherlands students were interviewed who were not part of the first sample (newcomers). These students were integrated in the school classes between the survey waves. The main questionnaire and the social integration within the class context (sociometric questionnaire) were measured repeatedly.

The data set of the third wave includes re-interviews with students from the first wave or the second wave. Additionally, 10 students are included who were part of the class list of the first wave, and therefore form part of the first wave’s target population, but were absent at the days of the school surveys in wave 1 and wave 2.

The main questionnaire was measured repeatedly.

In addition, two aspects of social integration were measured: (1) social integration outside the class context by means of egocentric networks and (2) social integration within the class context by means of a sociometric questionnaire (only in NL).

The survey instrument includes country-specific variations. The questionnaire also varies between various modules. For more information, see the study documentation.

Cognitive-cultural integration: language (objective measures of proficiency in the host country’s language, self-assessed knowledge of L1, self-assessed knowledge of L2, language use, language spoken at home); measurement of cognitive skills; leisure time activities (memberships, leisure time activities); number of books at home.

Structural Integration: school performance (self-assessment, grades, setting system, school type, repeating classes, private lessons); attitudes towards school (favourite subjects, educational aspirations, self-efficacy, anti-school norms, efforts in school, value of education, status maintenance motive, teacher support, satisfaction with school, success probabilities, perceived association between educational and occupational success, expected discrimination, financial restrictions, educational costs); economic situation (side job, pocket money, possessions, expected development of own economic situation); deviant behaviour and delinquency (school-related problem behaviour, delinquent behaviour).

Social Integration: sociometric information within classrooms; strong ties (ethnic background of friends); contact person in case of problems; person one is having trouble with; weak ties (in school, neighbourhood, clubs/associations); discrimination (victimisation in school, perceived discrimination); attitudes towards other ethnic groups; romantic relationships (characteristics of partner and relationship, expectations about the future of the relationship), family relations (parental support, parent-child contact, family cohesion, parental expectations, family conflict, embeddedness/influence of parents).

Emotional-cultural Integration: identity (with respect to host, respectively sending country, importance of ethnic identity); attitudes towards integration; religion (religious affiliation, importance of religion, religious practices); attitudes and norms (gender roles, violence legitimizing norms of masculinity, tolerance).

Health and well-being: personality and psychological well-being (life satisfaction, self-esteem, behavioural problems, self-control); health (general health status, health problems, sleeping...

The National Survey of American Life Adolescent Supplement (NSAL-A), 2001-2004, was designed to estimate the lifetime-to-date and current prevalence, age-of-onset distributions, course, and comorbidity of DSM-IV disorders among African American and Caribbean adolescents in the United States; to identify risk and protective factors for the onset and persistence of these disorders; to describe patterns and correlates of service use for these disorders; and to lay the groundwork for subsequent follow-up studies that can be used to identify early expressions of adult mental disorders. In addition and similar to the NSAL adult dataset (Collaborative Psychiatric Epidemiology Surveys (CPES), 2001-2003 United States), the adolescent dataset contains detailed measures of health; social conditions; stressors; distress; racial identity; subjective, neighborhood conditions; activities and school; media; and social and psychological protective and risk factors. Numerous variables from the adult dataset have been merged into the adolescent dataset, as the NSAL adult and adolescent respondents reside in the same households. Some of these variables apply to the entire household (i.e. region, urbanicity, and family income), while others apply specifically to the NSAL adult respondent living in the adolescent's household (i.e. adult years of education, adult marital status, and adult nativity [foreign-born vs. US born]). The immigration measures were asked of Caribbean black adult respondents only. No comparable measures assess the immigration and generational status of the Caribbean black adolescent respondents. The adult dataset measures are merged into the adolescent dataset to assist in approximating these measures for adolescent respondents. The NSAL adolescent dataset also includes variables for other non-core and experimental disorders. These include tobacco use/nicotine dependence, premenstrual syndrome, minor depression, recurrent brief depression, hypomania, and hypomania sub-threshold. Demographic variables include age, race and ethnicity, ancestry or national origins, height, weight, marital status, income, and education level.

NSCR-Schoolproject is the umbrella for a research program with four partial studies, aimed at enhancing insights into the development of delinquency and other problem behavior of adolescents, and the role of peer network formation among students, psycho-social development, and reactions from the school on problem behavior. The NSCR Schoolproject aimed to enhance insights into the complicated relations between delinquency and other problem behaviors, psycho-social development, peer networks, and school informal interventions. Network data from the longitudinal survey (3 waves) of the NSCR Schoolproject. This is one of the main data files for this study. Data available in consultation with NSCR. Please contact the datamanager [datamanagement@nscr.nl]

The current panel describes and explains the life-courses of immigrant and native young adults in the Netherlands. The survey covers three central themes: (A) progress in school and in the labour market, (B) the development of norms, values, lifestyle and attitudes, (C) changes in social networks and social participation. This panel is the first of a 3-wave panel study CILS4EU, which follows these immigrant and native children at age 14, 15 and 16 in the Netherlands, England, Germany and Sweden. Later waves were conducted in the Netherlands under the CILSNL project. Wave 1 interviews respondents of around 14 years old.

The full version includes full available information for all variables and 4-digit postal code information.

When citing this data, please also cite the international data of which it is a part: Kalter, Frank, Anthony F. Heath, Miles Hewstone, Jan O. Jonsson, Matthijs Kalmijn, Irena Kogan, and Frank van Tubergen. 2016a. Children of Immigrants Longitudinal Survey in Four European Countries (CILS4EU) – Full version. Data file for on‐site use. GESIS Data Archive, Cologne, ZA5353 Data file Version 1.2.0, doi:10.4232/cils4eu.5353.1.2.0.