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Please note that all times are shown in the time zone of the conference. The current conference time is: 17th May 2024, 02:55:11am GMT

 
 
Session Overview
Session
08 SES 03 B: Trends and challenges in relation to youth wellbeing
Time:
Tuesday, 22/Aug/2023:
5:15pm - 6:45pm

Session Chair: Ros McLellan
Location: Joseph Black Building, A504 [Floor 5]

Capacity: 50 persons

Paper Session

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Presentations
08. Health and Wellbeing Education
Paper

What Burns Up Finnish Upper Secondary Students? Relations between Student Burnout and Increased Expectations for Success

Sirkku Kupiainen, Risto Hotulainen, Irene Rämä, Laura Heiskala

University of Helsinki, Finland

Presenting Author: Kupiainen, Sirkku

Student burnout has been one of the most pertinent foci of discussion around Finnish adolescent’s’ wellbeing since the turn of the millennium (e.g., Salmela-Aro et al., 2009). Even if the onset of the growing unwell-being coincides with young people’s increasing dependence on social media and concern for various global problems, the nametag set on the phenomenon has been school burnout. The term has been further accentuated since a 2018 reform of higher education student admission, which shifted the emphasis from an earlier entrance examination-based policy to admitting half of new students based on their results in the national end-of-upper-secondary-school matriculation examination. The reform is seen to increase students’ stress both during their studies and in the matriculation examination (see Kupiainen et al., 2016).

The current study is part of a wider research project on the impact of the 2018 reform on upper secondary schools and on students’ study choices and wellbeing. In the present paper, we explore the validity of the emphasis on the school as the basis of student burnout (see also Kosola, 2022). For that, we enriched the instrument measuring burnout with outside-of-school topics such as climate change and the current geo-political situation expected to darken young people’s views on the world around them.

Burnout was first diagnosed in care and other human service occupations, and primarily attributed to the emotional exhaustion caused by the work (Maslach et al., 2001). Originally an ill-defined empirical concept, burnout was soon found to comprise three core dimensions: exhaustion, cynicism and reduced efficacy. The construct was soon adopted also for upper secondary and tertiary level students. While adult burnout was seen to be fueled by the rapidly changing and increasing demands of working life, students’ increasing ill-being needed a differing base for explanation. After all, unlike employees threatened by burnout under demands coming from above, students are living through a period where the focus is on the process of building their own lives. By emphasizing this difference, we do not wish to imply that the expectations set on young people in today’s world – or even school – might not feel overpowering for many. However, not all researchers in the field are disposed to use the term burnout to describe the stress and psychosomatic symptoms ailing today’s youth (e.g., Moksnes et al., 2010; Schraml et al., 2011). Regardless the terminology used, researchers in the Nordic welfare states are especially active in the field. This can be found surprising in view of the Nordic comprehensive education systems’ emphasis on equality and wellbeing with a stress on supporting everyone’s learning.

Contrary to most other countries, the best school-related trend data in Finland does not regard learning outcomes but student health and wellbeing (THL; Vainikainen et al., 2017). Since the 2018 reform of higher education student admission, upper secondary students’ wellbeing and burnout have been an especially acute topic of discussion. By increasing the role of the matriculation examination in student admission, the reform is seen to increase students’ stress regarding both their course choices during upper secondary education and, subsequently, their choices for the subject-specific exams they will include in their matriculation examination.

In this paper, we will investigate students and teachers’ views on possible reasons for the much discussed student burnout (a ready-provided list covering school and out-of-school related factors and general issues). Secondly, we will relate students’ views to their course choices and attainment, and to the choice of exams they plan to include in their matriculation examination. Thirdly, we will look at student burnout through an adapted 9-item School Burnout Inventory covering the three dimension of exhaustion, cynicism and experienced inadequacy.


Methodology, Methods, Research Instruments or Sources Used
The data is from an ongoing (2022–2023) study on the impact of the 2018 student admission reform on upper secondary schools and students. The data comprise questionnaires for students, teachers, principals and guidance counsellors, register data on the sampled (N = 8,000) students‘ study choices and attainment, and additional focus-group interviews of students and teachers in five upper secondary schools. In the present paper, we will focus on the students’ and teachers’ views on student wellbeing and burnout in their survey responses (quantitative and open response) and the interviews. The survey presented all four respondent groups with the same set of statements regarding possible reasons for student burnout. In addition, students were presented with a 9-item SBI.

Reflecting the cross-sectional survey data, the results will be mainly presented at the descriptive level, using ANOVA for analyses between groups (e.g., gender, students vs. teachers, low vs. high achievers) comparisons. The interview data will be used at this point to just provide ‘real-life’ examples of how the students and teachers see and talk about student burnout (results of the comparison of students’ and teachers’ views were used as a basis for the focus-group discussions).


Conclusions, Expected Outcomes or Findings
The preliminary data (4,000 students, 120 teachers) support the view that school-related reasons are seen central for student burnout: stress caused by the matriculation examination, the demands of upper secondary studies, and stress caused by university admission (mean 5.50/5.29, 5.74/5.31 and 5.14/5.80, respectively, for students and teachers on a 1–7 Likert scale). The groups also agreed on the role of lack of sleep (5.26/5.98) with teachers stressing this more. Teachers and students’ views differed most regarding students’ inability to free themselves from continuous social media use and digital gaming (6.06/4.13 and 5.66/3.43). Somewhat surprisingly, teachers saw climate change as a much stronger reason for student burnout than students did (4.80/3.58). The current upper secondary student data conformed only weakly to the predicted structure of the SBI, used in Finland earlier in tertiary education (Salmela-Aro, 2009). Reasons for this will be further explored in the presentation with full data. There was, however, a statistically significant gender difference in all dimensions with girls showing higher levels of exhaustion, cynicism and experienced inadequacy than boys (mean 4.64/3.58, 3.65/3.29 and 4.51/3.64, respectively, p<.001, ƞ2=.115, .011 and .073).
References
Kupiainen, S., Marjanen, J., & Hautamäki, J. (2016). The problem posed by exam choice on the comparability of results in the Finnish matriculation examination Journal for Educational Research Online, 8(2), 87.
 Maslach, C., Schaufeli, W.B., & Leiter, M.P. (2001). Job burnout. Annual Review of Psychology, 52(1), 397-422.
 Moksnes, U.K., Moljord, I.E., Espnes, G.A., & Byrne, D.G. (2010). The association between stress and emotional states in adolescents: The role of gender and self-esteem. Personality and Individual Differences, 49(5), 430-435.
 Salmela-Aro, K. (2009). Opiskelu-uupumusmittari SBI-9 yliopisto- ja ammattikorkeakouluopiskelijoille. Ylioppilaiden terveydenhoitosäätiö.
 Salmela-Aro, K., Kiuru, N., Leskinen, E., & Nurmi, J. E. (2009). School burnout inventory (SBI): reliability and validity. European Journal of Psychological Assessment, 25(1), 48.
 Schraml, K., Perski, A., Grossi, G., & Simonsson-Sarnecki, M. (2011). Stress symptoms among adolescents: The role of subjective psychosocial conditions, lifestyle, and self-esteem. Journal of Adolescence, 34(5), 987-996.
 THL (no date). School Health Promotion Study. Finnish institute for health and welfare. https://thl.fi/en/web/thlfi-en/research-and-development/research-and-projects/school-health-promotion-study
 Vainikainen, M.-P., Thuneberg, H., Marjanen, J., Hautamäki, J., Kupiainen, S., & Hotulainen, R. (2017). How do Finns know? Educational monitoring without inspection and standard setting. In Standard Setting in Education (pp. 243-259). Springer, Cham.


08. Health and Wellbeing Education
Paper

Chronotype, Sleep and Digital Media Use in Adolescence

Laura Kortesoja1, Ilona Merikanto2

1Centre for Educational Assessment CEA, Faculty of Educational Sciences, University of Helsinki, Finland; 2Department of Psychology and Logopedics and SleepWell Research Program, Faculty of Medicine, University of Helsinki, Finland

Presenting Author: Kortesoja, Laura

Adolescent sleep has declined significantly over the past 20 years (Keyes, Maslowsky, Hamilton & Schulenberg 2015). Inadequate and poor-quality sleep appears to be associated with both reduced motivation (Zhao et al. 2019) and impaired cognitive abilities that are important for learning and academic performance (Hysing, Harvey, Linton, Askeland & Sivertsen 2016; Kuula et al. 2015).

Developmental hormonal changes shift the sleep-wake cycle towards eveningness during adolescence. Morningness starts to decline around 12 years of age, continuing until late adolescence and early adulthood (Roenneberg et al. 2007). Many adolescents have difficulty falling asleep at the desired time on school nights. A Finnish population-based study showed that the later adolescents went to bed, the lower their sleep quality and the greater their daytime sleepiness was. This in turn was reflected in lower school performance and motivation (Merikanto et al. 2013). The effects of sleep deprivation and poor sleep quality extend to all areas of life, including learning, motivation, and well-being. Moreover, inadequate and poor-quality sleep increases daytime sleepiness, which can be reflected in lower school performance and motivation.

The use of various screens in the evening is unfortunately common among young people, delaying their bedtime (Bartel, Gradisar & Williamson 2015). Digital media use affects young people through a variety of mechanisms, such as exposure to blue light (Crowley, Cain, Burns, Acebo & Carskadon 2015) and emotions which increase alertness (Scott & Woods 2019). Although circadian rhythms operate independently of environmental factors, artificial light can modify individual sleep-wake rhythms (Gooley 2008; Roenneberg, Daan & Merrow 2003). Daytime exposure to light is preferable, as exposure to light during the evening or night inhibits melatonin release in the evening, making it more difficult to fall asleep. Exposure to blue light before bedtime may also affect sleep architecture, for example by shortening REM (rapid eye movement) sleep (Higuchi et al. 2005), which is crucial for the development of the young brain and also affects the ability to learn new things (Li et al. 2017).

The use of digital media devices both during the day and at night has been associated with insufficient sleep in previous studies. It is therefore important to investigate how young people's circadian rhythms and sleep are associated with the use of digital devices and apps. This study targeted to investigate how sleep and circadian rhythms are related to digital technology use at school and during leisure time. Q1: How do sleep and fatigue during the school week differ across chronotypes? Q2: How is the amount of use of digital devices or apps during schooldays and leisure time associated with sleep and fatigue in adolescents?


Methodology, Methods, Research Instruments or Sources Used
The data consisted of population-based longitudinal data from three measurement points (gathered in 2021-2022) and an experience-based sample from a Finnish school during one school week. The DigiVOO longitudinal study followed adolescents in grades 7-9 (n = 6522). The number of respondents in the experience sample was n = 140. The one-week data collection for the experience sample was carried out in February 2022. Adolescents received mobile questionnaires after each lesson to assess their motivation and well-being.

Circadian rhythms were assessed with a single question from the Morningness-Eveningness Questionnaire (Horne & Östberg 1976): “There are so-called morning-types and evening-types, which group do you belong to?”. A minority of respondents in the experience survey consider themselves to be definitely morning-types or more morning than evening-type compared to the other chronotypes. Day types were reported by around 20% of both the experience sample and the follow-up data. The amount of evening types was pronounced in both data sets. Almost half of the young people in the experience sample and just over a third of the young people in the follow-up data reported being more evening than morning types. Around one-fifth of the adolescents in the experience sample and just over a quarter of the young people in the follow-up sample considered themselves to be definitely evening-type.
Respondents of the experience sample (n = 140) reported their bedtimes and wake-up times for one school week. These were used to calculate the average length of sleep over the follow-up week. Fatigue in school mornings and days was measured by the question "Are you tired in school mornings/school days?" Sleep quality was measured by the question "How did you sleep last night?" School-related stress was measured by the question "Is your sleep interrupted because of school issues?". The use of digital media was measured by asking at the end of each lesson for a total of one school week whether and how many digital apps or devices were used in the lesson. Adolescents were also asked how many hours in total they spend per day playing games, watching videos, series, or movies, searching for information or following news online, connecting with friends, using social media, and creating content on social media.

As the group sizes were relatively small, differences in bedtimes and sleep duration between different chronotypes were examined using the Kruskall-Wallis H-test. Differences between chronotypes in sleep quality, fatigue, and school stress were examined using the chi-squared test.


Conclusions, Expected Outcomes or Findings
The most important finding from the follow-up data on leisure time use of digital devices and apps was that adolescents who reported themselves as definitely evening types were at higher risk than other chronotypes for more extensive use of digital devices and apps in leisure time, especially for watching videos, series or movies, using social media and actively communicating on apps. In this study, evening-types spent most of their time on digital media use in the form of watching videos, series or movies, social media, or active communication in apps. The finding supports previous research findings. As evening types may be chronically out of sync with their circadian rhythm, they may be at higher risk for the effects of late-night digital media use, especially in terms of sleep quality. Adolescents have been reported to be particularly vulnerable to the negative effects of screen time on a good night's sleep (Quante et al. 2019). This study also confirms previous findings that sleep problems are common among evening youth (Merikanto et al. 2017; Roeser et al. 2012).

It is important to raise awareness of the importance of different circadian rhythms and sleep for young people's well-being and learning. The shift in circadian rhythms towards eveningness is most pronounced in adolescence. Most young people are naturally evening-types, which makes it particularly difficult to fall asleep at the desired time to get enough sleep before the school day begins. For this reason, evening media use is concentrated in this group.


References
Bartel, K. A., Gradisar, M., & Williamson, P. (2015). Protective and risk factors for adolescent sleep: A meta-analytic review. Sleep Medicine Reviews, 21, 72–85. https://doi.org/10.1016/j.smrv.2014.08.002

Crowley, S. J., Cain, S. W., Burns, A. C., Acebo, C., & Carskadon, M. A. (2015). Increased Sensitivity of the Circadian System to Light in Early/Mid-Puberty. The Journal of Clinical Endocrinology & Metabolism, 100(11), 4067–4073. https://doi.org/10.1210/jc.2015-2775

Gooley, J. J. (2008). Treatment of circadian rhythm sleep disorders with light. Ann Acad Med Singapore, 37(8), 669-676.

Hysing, M., Harvey, A. G., Linton, S. J., Askeland, K. G., & Sivertsen, B. (2016). Sleep and academic performance in later adolescence: Results from a large population-based study. Journal of Sleep Research, 25(3), 318–324. https://doi.org/10.1111/jsr.12373

Keyes, K. M., Maslowsky, J., Hamilton, A., & Schulenberg, J. (2015). The Great Sleep Recession: Changes in Sleep Duration Among US Adolescents, 1991-2012. PEDIATRICS, 135(3), 460–468. https://doi.org/10.1542/peds.2014-2707

Kuula, L., Pesonen, A.-K., Martikainen, S., Kajantie, E., Lahti, J., Strandberg, T., Tuovinen, S., Heinonen, K., Pyhälä, R., Lahti, M., & Räikkönen, K. (2015). Poor sleep and neurocognitive function in early adolescence. Sleep Medicine, 16(10), 1207–1212. https://doi.org/10.1016/j.sleep.2015.06.017

Merikanto, I., Lahti, T., Puusniekka, R., & Partonen, T. (2013). Late bedtimes weaken school performance and predispose adolescents to health hazards. Sleep Medicine, 14(11), 1105–1111. https://doi.org/10.1016/j.sleep.2013.06.009

Roenneberg, T., Daan, S., & Merrow, M. (2003). The art of entrainment. Journal of biological rhythms, 18(3), 183-194.

Roenneberg, T., Kuehnle, T., Juda, M., Kantermann, T., Allebrandt, K., Gordijn, M., & Merrow, M. (2007). Epidemiology of the human circadian clock. Sleep Medicine Reviews, 11(6), 429–438. https://doi.org/10.1016/j.smrv.2007.07.005

Scott, H., & Woods, H. C. (2019). Understanding Links Between Social Media Use, Sleep and Mental Health: Recent Progress and Current Challenges. Current Sleep Medicine Reports, 5(3), 141–149. https://doi.org/10.1007/s40675-019-00148-9

Zhao, K., Zhang, J., Wu, Z., Shen, X., Tong, S., & Li, S. (2019). The relationship between insomnia symptoms and school performance among 4966 adolescents in Shanghai, China. Sleep Health, 5(3), 273–279. https://doi.org/10.1016/j.sleh.2018.12.008


08. Health and Wellbeing Education
Paper

‘Kids These Days!’ A Cross-Temporal Meta-Analysis of Changes in Emotional and Behavioral Problems Among Population-Based Samples of European Children

Boglarka Vekety1, Tamás Kói2,6, Alexander Logemann3, John Protzko4, Zsofia K. Takacs5

1Institute of Education, Eötvös Loránd University, Hungary; 2Translational Medicine Institute, Semmelweis University, Budapest, Hungary; 3Institute of Psychology, Eötvös Loránd University, Hungary; 4Department of Psychological Science, Central Connecticut State University, Connecticut, United States of America; 5School of Health in Social Science, University of Edinburgh, Scotland, United Kingdom; 6Department of Stohastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary

Presenting Author: Vekety, Boglarka

It is a common belief that new generations of children are in decline (Protzko & Schooler, 2019). However, contrary to the belief that new generations decline, children’s ability to delay gratification, measured by the famous ‘Marshmallow Test’, for example, has actually improved from the 1960s to the 2010s (Protzko, 2020). This means that (at least when it comes to food) young children today can resist rewards for a longer time than they did 50 years ago. This might be mainly due to an improvement in the living standards of families, an increase in parental educational level, the number of years children spend in education, and improvements in nutrition and healthcare services (Protzko, 2020). This increase in delay of gratification ability is similar to the ’Flynn effect’ which refers to the sustained increase in intelligence and cognitive domains worldwide during the 20th century (Pietschnig & Gittler, 2015). However, since the 2000s, stagnation or even reversal in intelligence (Dutton et al., 2016), attention, and working memory (Graves et al., 2021; Wongupparaj et al., 2017) have been observed in many countries worldwide, named as the ’negative Flynn effect’.

Along with the ’negative Flynn effect’, there have been other problematic trends reported among young adults, for example, self-reported loneliness has been increasing (Buecker et al., 2021), and emotional intelligence-related traits like well-being, self-control, and emotionality have been decreasing (Khan et al., 2021), with a similar negative trend in resilience (Zhao et al., 2022). These changes over the last decades have been proposed to be caused by complex twofold changes on the level of individuals and their environment (Buecker et al., 2021; Graves et al., 2021; Khan et al., 2021; Zhao et al., 2022). According to the mutual constitution model (Markus & Kitayama, 2010), the socio-cultural environment of different time periods is likely to shape the individual with its problems and vice-versa.

Importantly, these negative trends affecting the mental health of youth can be reversed with targeted interventions or prevention programs in education and/or healthcare. Yet, there is a lack of comprehensive research about time trend changes in childhood and adolescence regarding emotional and behavioral problems. Consequently, the aim of the present meta-analysis was to explore cross-temporal changes in the emotional and behavioral functioning of European children and adolescents and reveal if there are any specific problems that are on the rise and require immediate attention.


Methodology, Methods, Research Instruments or Sources Used
Studies that used the famous cross-culturally validated Child Behavior Checklist (CBCL) by Achenbach and Edelbrock (1983), or Achenbach’s Teacher-Reported Form (TRF), or the Youth Self-Reported (YSR) version of the checklist with population-based representative samples of 1-18 years old children were systematically searched in four databases (i.e., Web of Science, Scopus, Google Scholar, PubMed). For inclusion in this cross-temporal meta-analysis, studies had to report the raw means and standard deviations of the CBCL, TRF, or YSR. As the checklist has changed it’s possible maximum points in 2001, the percentage of possible maximum points (POMP) were calculated from the raw means and standard deviation (Buecker et al., 2021).
The inclusion criteria were set for only European population-based samples of youth for this analysis. The systematic search and selection procedure was performed by the main author and trained research assistants. There were more than 4000 studies screened by their title and abstract, and after the full-text selection only 58 remained for this analysis.

Conclusions, Expected Outcomes or Findings
For the identification of any non-linear changes due to children’s age, three age groups were set: early childhood (1-6 years), middle childhood (7-14 years), and teens (14-18 years). Mixed-gender meta-regressions showed a significant increase in somatic complaints, such as headache or stomachache without a medical cause, in early childhood (k = 5, b year = 0.385, p = .03) and middle childhood (k = 5, b year = 0.216, p = .05) over the last decades according to parents opinion, and a marginally significant increase in adolescence as well (k = 6, b year = 0.209, p = .06). Parents also reported a large increase in 1-6 years old children’s externalizing problems, more specifically aggression and attention problems, over the last 20 years (k = 10, b year = 0.626, p = .04). Among 7-14 years old children the same externalizing problem subscale, but for this age group it involves aggression and deviant behavior, showed a significant decrease (k = 22, b year = -0.375, p = .05) as reported by parents. The meta-regression analysis of teenage samples showed a significant increase in anxious-depressed problems over the last decades according to parent reports (k = 7, b year = 0.310, p = .02), but a decrease in aggression according to youth’s self-report (k = 7, b year = -0.601, p = .03).
When girls and boys were analyzed separately, mete-regression revealed an increase in 7-14 years old European boys’ attention problems (k = 10, b year = 1.087, p = .01), and a somewhat smaller increase in European girl’s attention problems (k = 10, b year = 0.884, p = .02). Gender-specific differences were found in the change of social problems in middle childhood: girls showed a significant increase in social problems over time (k = 9, b year = 0.620, p = .05), while the increase in such problems among boys was non-significant (k = 9, b year = 0.498, p = .09).

References
1.Achenbach, T. M., & Edelbrock, C. S. (1983). Manual for the child behavior checklist and revised child behavior profile. Department of Psychiatry, University of Vermont.
2. Protzko, J. & Schooler, J. W. Kids these days: Why the youth of today seem lacking. Sci. Adv. 5, eaav5916 (2019).
3.Protzko, J. Kids These Days! Increasing delay of gratification ability over the past 50 years in children. Intelligence 80, 101451 (2020).
4.Pietschnig, J. & Gittler, G. A reversal of the Flynn effect for spatial perception in German-speaking countries: Evidence from a cross-temporal IRT-based meta-analysis (1977–2014). Intelligence 53, 145–153 (2015).
5.Dutton, E., van der Linden, D. & Lynn, R. The negative Flynn Effect: A systematic literature review. Intelligence 59, 163–169 (2016).
6.Graves, L. V. et al. Cohort differences on the CVLT-II and CVLT3: Evidence of a negative Flynn effect on the attention/working memory and learning trials. Clin. Neuropsychol. 35, 615–632 (2021).
7.Wongupparaj, P., Wongupparaj, R., Kumari, V. & Morris, R. G. The Flynn effect for verbal and visuospatial short-term and working memory: A cross-temporal meta-analysis. Intelligence 64, 71–80 (2017).
8.Buecker, S., Mund, M., Chwastek, S., Sostmann, M. & Luhmann, M. Is loneliness in emerging adults increasing over time? A preregistered cross-temporal meta-analysis and systematic review. Psychol. Bull. 147, 787 (2021).
9.Khan, M., Minbashian, A. & MacCann, C. College students in the western world are becoming less emotionally intelligent: A cross-temporal meta-analysis of trait emotional intelligence. J. Pers. 89, 1176–1190 (2021).
10.Zhao, Z., Wan, R. & Ma, J. Social change and birth cohorts decreased resilience among college students in China: A cross-temporal meta-analysis, 2007–2020. Personal. Individ. Differ. 196, 111716 (2022).
11.Markus, H. R. & Kitayama, S. Cultures and selves: A cycle of mutual constitution. Perspect. Psychol. Sci. 5, 420–430 (2010).


08. Health and Wellbeing Education
Paper

Popularity and the Propensity for Prosociality: The effect of Social Status on Social Behaviour

Yael Malin

The Hebrew University of Jerusalem, Israel

Presenting Author: Malin, Yael

How to increase rates of school prosocial behavior is an abiding concern to society and is of considerable interest to educational scholars and stakeholders. In the last decades, research on prosociality in schools focuses on social interactions among children, from the very early stages of development onward. It was found that factors such as peer relations, group affiliation, and social status, may prevent or activate prosocial behavior (Sabato & Kogut, 2021)– with significant implications for school climate, academic success and personal well-being (Schonert-Reichl, 2017). One of the salient findings in this field of research is that higher-status children are perceived by their friends and teachers to be more helpful, cooperative, and kind (van den Berg et al., 2015), and tend to a sharing behavior more than lower-status children (Sabato & Kogut, 2021). However, this link is not straightforward (Warden & MacKinnon, 2003) and contextual variables that determine when social status encourages or hinders prosociality should be examined.

It has been suggested that the effect of social status on prosociality may be dependent on the characteristics of the beneficiary and that children are more prosaically toward their in-group members, known as in-group favoritism (e.g., Sabato & Kogut, 2021). This effect may be even more substantial when the beneficiary is from a stereotyped group (Zimmerman & Levy, 2000). Nevertheless, several contextual factors may attenuate in-group preference. For example, in-group favoritism was found to be significant only among children in the higher social status group (Newheiser et al., 2014; Sabato & Kogut, 2021). In addition, introducing specific out-group stereotypes, increased the incidents of children helping a needy out-group member more than an in-group member, although the children held a negative conception of the out-group member (Sierksma, 2022). These findings, altogether, indicate that social status and the beneficiary’s characteristics have an interaction effect on prosocial behavior.

The current study aims to understand the effect of social status on social orientation toward in-group versus out-group members, and toward stereotyped versus neutral individuals, among children between the ages of ten to eleven, since social status becomes relatively stable from the fourth grade onward (Poulin & Chan, 2010). Although previous studies pointed to the relationship between social status and prosociality, along with the effect of such situational factors, most of them are based on evaluations by peers and teachers of the children’s general tendencies of prosociality or self-report, rather than measure overt behavior. In order to examine overt behavior, we use the Social Mindfulness paradigm (SoMi; Van Doesum et al., 2013) which provides individuals with a choice between a mindful/ cooperative decision and a self-centered decision.

Several studies applied this task among adults and showed that the socially mindful person is also scored high in the HEXACO personality inventory which measures factors related to respect for others and their perspective on the world, and other-oriented intention (i.e., honesty-humility, agreeableness, fairness, sentimentality, forgiveness, flexibility). This finding supports the idea that social mindfulness is rooted in benevolent prosocial motivations (Van Doesum et al., 2013). Only one study, to date, used this task among children and examined judgments of a third party’s behavior in hypothetical scenarios (see Zhao et al., 2021). This study indicated that, by age 6, children understand the task and its meaning, and positively evaluate a character who takes a snack for herself in a way that leaves a choice for others over a character who leaves no choice. The present study will be the first to utilize the SoMi task and examine self-oriented versus other-oriented decision-making among children while considering their social status in the class.


Methodology, Methods, Research Instruments or Sources Used
A power analysis using G-Power (α=.05, power = 0.95) indicated that a sample of 236 participants would allow to detect a small-to-medium effect size (f2=.10). We recruited 300 fourth and fifth-grade children attending schools in Israel. The experiment includes two stages held several weeks apart, to prevent common method bias (Podsakoff et al., 2012).

First stage - Social Status Measuring
This stage takes part in a classroom setting. The experimenter writes all children’s names with serial numbers on the board. Through a questionnaire, participants indicate regarding each other child whether or not they typically play with him during school breaks, meet him after school, and tell him personal things. Each child is ranked according to the number of reported interactions with him. This measure was used and validated in previous research (see Sabato & Kogut, 2021).

Stage Two - The SoMi Task
This stage is conducted in individual settings, where trained experimenters interview each child privately. Participants are randomly assigned to one of four conditions, manipulating the beneficiary’s group affiliation (in-group—a child from their class/out-group—a child from another equivalent grade class); and stereotype (stereotyped—a child immigrant /non-stereotyped—no information provided). The experimenter introduces the SoMi as a decision task in a dyadic interaction with another child and gives them details regarding this child according to their condition, as priming to the task. Then, she explains that they choose first from several categories (e.g., cupcakes, hats, pens) one of three objects that they would get to take home, while the other child chooses from the two remaining objects. Six categories randomly appear on a computer screen; per each, two objects are entirely identical, and the third is unique in its color (colors appear randomly). Choosing the object of which there are two, and providing the other child with two options, would be scored as mindful (1). Choosing the unique option would be scored as unmindful (0). The final score is an average of all rounds, scaling between 0 (only unmindful choices) and 1 (only mindful choices).
*Editing addition - a pilot study has led us to a methodological issue with the SoMi task, therefore we made adjustments in our study and adopted the Public Goods Game that measures sharing within the group. We measure through the game whether children prefer to keep prices for themselves or share with their class to optimize their class’s benefits.

Conclusions, Expected Outcomes or Findings
The present study aims at deepening our understanding of the association between social status and prosocial behavior among children, by proposing the intergroup and stereotype contexts as possible factors in attenuating this association. Through the SoMi task, we examine other-oriented versus self-oriented decision-making, while participants are entirely autonomous to choose for themselves.

This study is ongoing, therefore, the final data, including participants’ distribution is unavailable. Our main hypothesis is that higher social status children will tend to other-oriented decision-making, while lower social status children will tend to self-oriented decision-making. Our secondary hypothesis, in line with previous research (Sabato & Kogut, 2021) on sharing behavior, is that higher social-status children will have higher levels of in-group favoritism, relative to lower social-status children. However, the information regarding the stereotype may increase other-oriented decisions toward out-group members among both, the lower and higher social-status children.

Since people may help due to non-altruistic motives such as conforming to social norms and reducing one’s own negative arousal, known as empathic distress (Gugenishvili & Colliander, 2022), we also examine the question of the underlying motivation through subsequent questions in an interview setting after the SoMi task. Lower social-status children are expected to be motivated by self-focused considerations (e.g., fear of being excluded, identification with a child who is also in a lower social status), while higher social-status children are expected to be motivated by other-focused considerations (e.g., mutuality, empathy) (Sabato & Kogut, 2021).

Future research might use an experimental design that manipulates children’s social status situationally. This can be done through cyberbullying or imaginary tasks (see Nesdale et al., 2009) and examination of social orientation toward different beneficiaries (in-group vs. out-group, and stereotyped vs. neutral). Such manipulation would allow for more causal conclusions regarding the effect of the experience of social exclusion on social orientation.


References
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