The relation of school absenteeism with adolescent profiles of health behavioural-, psychosocial- and socio-economic characteristics: a cross-sectional study

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Complexities arise from the multitude and clustering of these risk factors, challenging recognisability of adolescents at risk. This study aimed to identify usable adolescent profiles based on health behavioural-, psychosocial- and socio-demographic characteristics. School absenteeism outcomes of these profiles were compared. METHODS: A total of 5,889 Dutch secondary school students completed a self-report questionnaire on (1) physical- and mental health, (2) health-related behaviours, and (3) school-, social- and family environments. Profiles of adolescents with similar characteristics were identified using a latent profile analysis. School absenteeism rates and reasons for absenteeism were compared among the identified profiles. RESULTS: Four profiles were identified: profile A (10.1%), profile B (17.3%), profile C (60.7%) and profile D (11.9%). Two profiles (A, D) showed increased absenteeism risks, each displaying a combination of somatic, mental, social, and family-related problems. Profile A was characterized by psychosocial problems, while profile D was characterized by risk-taking behaviors. Both profile A and D were present across all educational levels. CONCLUSIONS: The clustering of health behavioural, psychosocial, family and socioeconomic problems implies that school absenteeism requires a comprehensive approach, focusing not only on individual risk factors but also on the interaction of risk factors in an adolescent’s life. Knowledge about our identified profiles can be used to better recognize adolescents at risk and to tailor current interventions in practice, in order to decrease the burden caused by school absenteeism. school absenteeism adolescents psychosocial behaviour substance use health behaviour family Figures Figure 1 INTRODUCTION School absenteeism is a common problem both in the Netherlands as worldwide, in the Netherlands defined as ‘more than 16 hours of absence in the past four weeks’ 1 . Approximately 5% of all Dutch adolescents absented more than 16 hours in 4 weeks during the schoolyear of 2018-2019 2 . The term ‘School absenteeism’ comprises several terms, including ‘school refusal’, ‘truancy’, and ‘sickness absence’. School refusal refers to a child-motivated refusal to attend school 3 . Truancy refers to a school absence due to inexcusable reasons, e.g. to pursue stimuli such as illegal activities, delinquency or gaming 5 . Several risk factors are associated with school absenteeism, but school absenteeism itself is also a predictor for various problems, such as adult psychosocial problems, poor school performance and increased risk for school drop-out 6 . This aspect emphasises the importance of identifying adolescents who are at risk in time. However, identifying adolescents at risk for absenteeism and elucidating the risk factors for school absenteeism are complex due to several wide-range risk factors associated with each of them 3,7,8 . Berends and Van Diest 7 provided an overview of risk and protective factors for school absenteeism and categorised them into the following five domains: the adolescent, home environment, peers, school and context. The domain adolescent spans risk factors like physical health problems, mental health difficulties and unhealthy behaviours 9 . The domain home environment spans risk factors related to poverty and family conflicts 9 . The domain peers spans peer-related risk factors, which influence increases especially during middle school 7 . The domain school environment spans factors like boredom with school, poor school climate, low-quality teachers and inadequate education 7 . Finally, the domain context spans risk factors like low socio-economic status and neighbourhood characteristics 7 . School absenteeism is also a predictor of various health problems. Previous research found that (mental) health problems, social behaviours and health behaviours, including school absenteeism, tend to cluster in adolescence 10,11,12,13 . Results of these studies indicate that risk-seeking behaviours tend to cluster together, including alcohol use, drug use, smoking, risky sexual behaviour and delinquent behaviour 14,15,16,17 and that sedentary behaviours is another group of behaviours that tend to cluster together, such as low physical activity, poor nutritional intake and increased daily screen time 18,19 . However, these studies have only limitedly addressed factors in the contextual and school domains. Moreover, they often lack information on internet and social media use, which is considered a new concern for adolescent health. Therefore, evidence from old clustering studies may not be representative, as they include TV and computer screen time instead of current internet behaviour 20 . Only recently have studies also included social media and smartphone use. A systematic review 21 showed that all social media domains—time spent, activity, investment and addiction—are associated with depression, anxiety and psychological distress, which emphasises the importance of updating the knowledge about current internet-related behaviour. In conclusion, previous studies on clustering found that the co-occurrence of social, behavioural and (mental) health problems is associated with increasingly adverse psychosocial and physical health outcomes. However, how these findings relate to school absenteeism remains unclear. Current public health interventions usually target a single type of behaviour in isolation 7 . In addition, effectiveness of these interventions varies depending on the co-occurring problems of the adolescent 22 . Interventions focusing on multiple domains have been proven to be most effective for adolescents with multiple health problems 23 . In summary, the literature on school absenteeism provides information about individual risk factors and less about the clustering of risk factors. Studies that inspect the clustering of problems in adolescence mostly include only a limited number of factors and do not examine its relation to school absenteeism. The aim of the current study is therefore to explore whether specific adolescent profiles can be identified based on a broad range of characteristics and to assess how these profiles are related to school absenteeism. The research questions in this study are 1) ‘Can profiles be identified within adolescents based on characteristics related to the adolescent, family environment and school environment?’, 2) ‘What is the incidence of school absenteeism in the identified profiles?’, and 3) ‘Are there differences in reasons for absenteeism between the identified profiles?’. METHODS This study concerns a cross-sectional observational study using routinely collected data from preventive youth health care services. The legal review board of the municipal health centre approved the use of their database for this study. Procedure The data concern self-report questionnaires administered in school years 2016–2017 and 2019-2020 in the municipality of The Hague. As part of a pre-existing preventive health check (JongerenConsult Check Up), students receive an invitation to take the JongerenConsult questionnaire in either class 3 or 4 of secondary school. This questionnaire aims to monitor and improve the health of adolescents. Students in grade 3 or 4 are usually 14-16 years old. For the check-up, adolescents are invited by e-mail to complete the questionnaire. They are informed about the aim and procedure of the check-up, both by e-mail and during class hours. Adolescents have to give informed consent. Parents are also informed by e-mail and have the option to object to participation. Adolescents complete the questionnaire during class hours. Based on their answers in the questionnaire, adolescents with an increased risk of adverse (mental) health outcomes receive an invitation for a consult with the school nurse. Adolescents are informed that the questionnaire data and consultations are strictly confidential. Participants The sample is a census of all schools for secondary education situated in the area The Hague. The Hague is the third largest city in the Netherlands with an ethnically diverse population (Dutch [44%], other Western nationality [19%], non-Western nationality [37%]) 24 . A total of 5,889 adolescents aged 14-16 completed the JongerenConsult questionnaire and were included in the analyses. The average age was 15 years. Gender was evenly distributed, and 48.2% were girls. Within the participant group, all represents all levels of education. Schools for special education were not invited to participate. The Dutch secondary education begins after elementary education, typically at the age of 12. Based on teacher advice and the results of the Cito test (final year assessment of elementary school), a choice is made for one of the following types of secondary education: 1) preparatory vocational secondary education (VMBO), having a practical subtype (VMBO-b/k) and a theoretical subtype (VMBO-t), 2) senior general secondary education (HAVO) and 3) university preparatory education (VWO) 25 . Instruments The questionnaire we used is comparable to the E-MOVO 26 , which is widely used in secondary schools during the adolescent preventive health check in the Netherlands. It includes questions about school, physical and mental health, health-related behaviours, and social and family contexts. Variables for analysis Outcome . School absenteeism was measured as the sum of truancy (hours) and sickness absenteeism (days). The typical duration of a school day is 8 hours. The outcome school absenteeism was categorised as follows: ‘0’ refers to ‘less than 16 hours of absence in the past four weeks’ and ‘1’ refers to ’16 or more hours of absence in the past four weeks’. This definition is based on Dutch governmental guidelines 4 . The analysis included 16 variables that together provided information on a mix of observable personal characteristics and latent measurements of the various adolescent domains. The variables were: gender (1 = ‘male’ and 2 = ‘female’), level of education (1 = ‘practical pre-vocational education’, 2 = ‘theoretical pre-vocational education, 3 = ‘senior general secondary education’ and 4 = ‘pre-university education’), eating and physical activity (4 items), alcohol use (3 items), substance use (3 items), sexual behaviour (2 items), physical health (2 items), mental health (measured by the total score on the Strengths and Difficulties Questionnaire [SDQ] 27 ), bullying behaviour (2 items), social media problems (7 questions), gaming problems (7 questions), family relations (4 items), adverse life events (3 items), financial problems (1 item), perceived safety (2 items) and school rating (1 item). The detailed information about the items is listed below in Appendix A. Among all item responses, on average, 0.4% of the answers were missing. A total of 3,809 and 2,080 students completed the 2016–2017 and 2019–2020 questionnaires, respectively. Missing data was imputed using the Multivariate Imputation via Chained Equations (mice) package in Rstudio. Missing data was imputed for the following variables: bullying, alcohol use, substance use, sexual behaviour, eating and physical activity, family relations, and adverse life events. The datasets before and after imputing were analysed to examine whether the outcomes differed. No significant differences were found. Reliability and scaling . Item response theory (IRT) was applied to construct the scales for all constructs that contained more than two questions. This method was performed in the Rstudio using the R package mirt 28 . The IRT analysis computes a weighted person score for each participant based on the item responses to all questions of a specific construct. In the estimation of the person scores, IRT gives items more weight than other items depending on the item difficulty (which is derived from the frequency of occurrence) and item discrimination (which is derived from the item-scale correlation). IRT was applied to the following measures: bullying, eating and physical activity, alcohol use, substance use, sexual behaviour, safety, adverse life events, and family relations. A-parameters were examined for each item, and each measure included items with similar a-parameters. Trace plots were examined to examine the distribution of chosen answer options to verify whether the categorised answer options were properly categorised. Reasons for school absenteeism To explore reasons for school absenteeism, we analysed the question ‘What is the most important reason for you to be absent from school?’. The participant had choice of 11 answer options and could answer the open answer box. Both multiple choice and open questions were analysed, and the answers were categorised as follows: stress, unsafe school environment, motivation, school-related, subject-related, psychosocial, learning difficulties and tiredness. Analysis of the answers was conducted separately by two researchers in order to minimalize researcher bias in the response. Statistical analyses First research question . A latent profile analysis (LPA) was performed in the Rstudio using the tidyLPA package. The indicator variables used for identifying profiles were gender, level of education, physical health, mental health, bullying, problems caused by social media, problems caused by gaming, eating and physical activity, alcohol use, substance use, sexual behaviour, family relations, adverse life events, safety, financial status and school rating. The LPA fits a number of models equal to the number of variables. The package MClust was used to derive information about model fit, including Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the model entropy. In general, lower AIC and BIC values indicate that the model fits the data better 29 . Entropy indicates how accurate the individuals were assigned to the latent profiles. Entropy varies from 0 to 1, where a higher value indicates a more accurate classification. AIC, BIC and entropy were used to determine the most accurate model. In the next step, the variation of students assigned to different profiles was examined. All values were standardised to a 0–1 scale by the ‘poms’ function in tidyLPA. The package ggplot2 was used to plot Graph 1, which shows how the groups scored on the various variables. Second research question . Independent sample t-tests and chi-square tests were used to compare the identified profiles regarding total school absenteeism, truancy, sickness absence and reasons for absenteeism. The alpha level was set to 0.01. Furthermore, Bonferroni corrections were used to correct for chance inflation. Insert Table 1 here RESULTS As stated in Table 1, adolescents who were absent for more than 16 hours were more often girls and reported more often chronic illness and medication use, poor eating and exercise behaviour, a moderately increased or increased SDQ score, problems caused by social media and gaming, alcohol and substance use, risky sexual behaviour, adverse life events, perceiving an unsafe environment, poor financial status and an adverse school climate compared with adolescents who were absent for less than 16 hours. Latent profile analysis A model with four profiles was estimated as the most adequate, with an entropy of 0.999. Appendix C shows the fit indicators of the first five models in the LPA. The identified four profiles were distributed as follows: profile A (10.1%), profile B (17.3%), profile C (60.7%) and profile D (11.9%). Two groups had overall high scores on most variables, and two groups showed low scores on most variables. The first group with increased absenteeism rates was defined as the psychosocial problem group (profile A), which is characterised by mental health problems (seen as a substantial increased SDQ score), more problems caused by social media and gaming, low school climate rating, more adverse life events, poorer family relations and more chronic illness and medication use than the other groups. The second group with high scores was defined as the risk-taking behaviour group (profile D), as these adolescents reported more often using alcohol and other substances and engaging in risky sexual behaviour than the other groups. The two groups with low scores on health and psychosocial behaviours and problems were divided into profile B, which only presents students following practical vocational education (less problem-vocational educated group), and profile C, which represents students following a theoretical education (less problem-theoretical educated group). Besides differences in education, the theoretical-educated group had better eating and physical activity habits and more alcohol and substance use than the vocational-educated group. Insert Table 2 here Insert Figure 1 here Absenteeism outcomes in the four identified profiles Reported truancy and sickness absence were on average 0.66 hours (SD 3.54) and 1.18 days (SD 2.21) in the past 4 weeks, respectively. Truancy and sickness absence rates were significantly higher in both problem groups. In addition, the vocational-educated group reported more truancy and sickness than the less problem theoretical-educated group. The percentage of students who were absent for more than 16 hours in the past 4 weeks was 27% in the total study population; however, these school absenteeism percentages varied markedly between the four profiles. Total absenteeism was 37.7% in the psychosocial problems group, 37.5% in the risk-taking behaviour group, 28.1% in the less problem-vocational educated group and 23.0% in the less problem-theoretical educated group. Insert Table 3 here Reasons for absenteeism The most reported reasons for absenteeism were motivation (70.6%), school-related reasons (44.0%), school subject-related reasons (23.8%) and learning difficulties (11.8%). Commonly reported in the open answer box were ‘feeling tired’ or ‘lessons begin too early’, although both were not represented in the 11 answer options. School absenteeism reasons were comparable in both problem groups, although the psychosocial problems group overall reported more reasons. Insert Table 4 here DISCUSSION This study aimed to explore whether adolescent profiles could be distinguished based on health behavioural-, psychosocial- and socio-economic characteristics, and how these profiles relate to school absenteeism. We identified two different adolescent profiles with more problems and two with fewer problems. In the psychosocial problem group (A), adolescents are characterised by an increased risk of mental health problems, chronic illness or medication use, problems caused by social media and gaming, financial problems, adverse life events, poor family relations and low school climate ratings. The risk-taking behaviour group (D) was in particular more likely to report risky sexual behaviour, use of alcohol and other substances, and also to a lesser extent, chronic illness or medication use and financial and family-relational problems. Total school absenteeism rates were above average in these two problem groups. The other groups (B, C) were characterised by fewer problems and had lower school absenteeism rates. The vocational-educated group (B) showed lower scores on eating and exercise behaviour compared with the theoretical-educated group (C), whereas the latter reported more alcohol and substance use than group B. The most reported reasons for absenteeism were motivation, school-related reasons, school subject-related reasons and learning difficulties. Our findings were in line with earlier studies; however, an exact comparison was not possible due to variations in the methods and in the included variables. The co-occurrence of alcohol and substance use, risky sexual behaviour and male gender found in profile D is, for example, often discussed in the literature 13,16,17,30,31 . In addition, the co-occurrence of sedentary behaviour 32 , screen time 33 , poor mental wellbeing and female gender 34 is previously described in the literature, which was consistent with the characteristics in profile A. Furthermore, consistent with the literature, we found that poor physical health is intertwined with problems in other domains 30,35 . Both our problem profiles (A, D) reported more chronic illness or medication use, in combination with increased risk of mental health problems in particular for profile A, risk-taking behaviour in particular for profile D, and socioeconomic problems for both profiles. In addition to other studies, we also included information about social media use, adverse life events and family relations, which were all clearly part of the psychosocial problem profile and, to a lesser extent, also of the risk-taking behaviour profile. When comparing the two profiles with fewer problems (B, C), the most important distinguishing characteristics between these two groups were educational level, physical activity and eating habits, and alcohol and substance use; the lower educated profile reported unhealthier physical activity and eating habits, whereas the higher educated profile reported more use of alcohol and other substances. These findings indicate that educational level is associated with poor eating habits and physical activity, which is consistent with other studies 36,37,38,39 . However, the frequent alcohol and substance use in the higher educational group is surprising because other studies on Dutch adolescents showed that these behaviours are more prevalent among lower educated students 40 . One hypothesis for this difference could be linked to the ethnicity ratios in The Hague being deviant from the Dutch average: 43% of adolescents in The Hague have a non-Western ethnicity compared with a Dutch average of 18% 41 . Adolescents with a non-Western ethnicity more often show unhealthy eating and physical activity habits but less often use alcohol and other substances, often with strict rules regarding these substances 40 . In addition, they also more frequently follow lower education 41 , which might explain this discrepancy with other studies. However, due to privacy reasons, we were not able to use the data regarding ethnicity and could not test this hypothesis. All included variables were associated with higher absenteeism rates, supporting current evidence of reviews 1,2 . Substance use and externalizing behaviour, roughly corresponding to our profile D, are the most predictive for school absenteeism 13 . However, our results also showed that a combination of problems is an indicator of increased school absenteeism rates. For example, unhealthy behaviours were more prevalent among absenting adolescents, but this factor was only applicable for adolescents in the psychosocial and risk-taking profiles, who more often reported problems on multiple domains. This knowledge could explain why interventions focusing on a single behaviour are less effective 9 . The lowest absenteeism profile C also had considerably low standard deviations for school absenteeism, suggesting a more homogeneous group; this result indicates that being a healthy adolescent with less psychosocial-, family- and socioeconomic problems and following higher educational levels is associated with consistent low absenteeism rates. Nevertheless, the lower educated group had lower absenteeism rates when having fewer problems in other domains. Although on the pre-university level, the profiles with higher absenteeism rates were somewhat less prevalent, and they were observed in all educational levels, suggesting that targeted preventive interventions should be integrated in all educational levels. Strengths and limitations of this study A strength of this study is the broad range of included variables, especially the information on current social media behaviour, family relations and school characteristics, which valuably improve the current knowledge. Our participating group is unique, which is not only large but also considerably represents the complete The Hague adolescent population, as all adolescents were invited to participate in this JongerenConsult questionnaire, which is used as the first step in a standard preventive health consultation. In addition, using IRT in calculating person scores strongly strengthens this study, ensuring that the distinctions found are clear and valid. Furthermore, the entropy of the four-profile model was very high (0.99), indicating that the LPA identified four distinct profiles. Limitations included the JongerenConsult questionnaire, which was not specifically designed for research purposes. Furthermore, due to our urban population, we cannot guarantee our results to be representative of average Dutch adolescents. Furthermore, we used quantitative data to explore reasons for absenteeism, but to clearly understand a complex and multifactorial problem this could best be supplied with qualitative research. Recall bias might have occurred, as all information were self-reported. However, an internal municipal health service study compared our questionnaire with an anonymous similar questionnaire and found that in the non-anonymous questionnaire, psychosocial problems were underreported, whereas alcohol use and school absenteeism were overreported. However, we expect that its impact on our study is limited, as this bias is present in all groups. CONCLUSIONS The total absenteeism prevalence in this study, which is 27%, indicates that school absenteeism forms an important concern for schools that cannot be ignored. The identified profiles provide extended information about adolescents at risk for school absenteeism. The identified problem profiles were associated with higher absenteeism rates and were present through all educational levels. The clustering of health, psychosocial, family and socioeconomic problems implicates that school absenteeism requires a comprehensive approach, focusing not only on individual risk factors but also on the interaction of risk factors in an adolescent’s life. Furthermore, the growing use of the internet and social media produces a new concern for adolescent health, and its co-occurrence with family problems may be an concerning combination that warrants further exploration. Abbreviations AIC: Akaike information criterion BIC: Bayesian information criterion. IRT: item response theory LPA: latent profile analysis MICE: multivariate imputation via chained equations Profile A: Psychosocial problems group Profile B: Less problems practical educated group Profile C: Less problems theoretical educated group Profile D: Behavioural problems group SDQ: strength and difficulties questionnaire Declarations Ethics approval and consent to participate The author of this study received permission from the legal department of Municipal Health service of The Hague for use of their database with anonymous school health check questionnaire information for research and publication purposes. The data was gathered by the Municipal Health Service of The Hague. Consent for publication Not applicable Availability of data and materials Due to the nature of the research, due to legal restrictions supporting data is not available. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions LK, MRC and RML designed the study and performed the data analyses. LK made the tables and figures. All authors participated in the interpretation of the findings and finalizing the paper. LK wrote the first draft of the article. MRC and RML critically revised all draft versions. The final draft was approved by all authors. All authors share equal accountability for the paper and provided final approval for the submitted manuscript. LK is the corresponding author. Acknowledgements Special acknowledgment goes to Y. Turfboer for assistance in obtaining and utilizing the anonymous school health check questionnaire database of the Municipal Health service of The Hague. Conflict of Interest Disclosure Statement All authors of this study declare they have no conflicts of interest. References Dienst Uitvoering Onderwijs. Soorten verzuim. 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Psychosocial and behavioural factors in the explanation of socioeconomic inequalities in adolescent health: a multilevel analysis in 28 European and North American countries. J Epidemiol Community Health. 2014;68:912–921. DOI:10.1136/jech-2014-203933 Huurre T, Aro H, Rahkonen O, Komulainen E. Health, lifestyle, family and school factors in adolescence: predicting adult educational level. Educ Res. 2006;48:1,41-53. DOI:10.1080/00131880500498438. Karvonen S, Rimpelä A. Socio-regional context as a determinant of adolescents' health behaviour in Finland. Soc Sci Med. 1996;43(10):1467-1474. DOI:10.1016/0277-9536(96)00044-5. De Vries H, van 't Riet J, Spigt M, Metsemakers J, van den Akker M, Vermunt JK, Kremers S. Clusters of lifestyle behaviors: results from the Dutch SMILE study. Prev Med. 2008;46(3):203-208. DOI:10.1016/j.ypmed.2007.08.005. Stevens G, Van Dorsselaer S, Boer M, et al. HBSC 2017: Gezondheid en welzijn van jongeren in Nederland. The Netherlands: Trimbos Instituut, 2018. Centraal Bureau Statistiek. VO; leerlingen, onderwijssoort, leerjaar, migratieachtergrond, generatie. Available at: StatLine - Vo; leerlingen, onderwijssoort, leerjaar, migratieachtergrond, generatie (cbs.nl). Accessed April 2024. Tables Table 1 – Adolescent characteristics sorted by school absenteeism (page 9) %n in total population <16h Absenteeism %n, n = 4244 ≥16h Absenteeism %n, n = 1574 Gender Boy 51.8 54.0 45.7 Girl 48.2 46.0 54.3 Level of education Practical pre-vocational education 23.7 22.0 26.8 Theoretical pre-vocational education 22.5 22.0 24.1 Senior general secondary education 26.8 26.9 27.0 Pre-university education 26.9 29.1 22.1 Physical health No medication use or chronic illness 67.9 70.9 60.0 Chronic illness or uses medication 13.5 12.5 16.0 Chronic illness and uses medication 18.6 16.6 24.0 Mental health Normal 87.1 89.4 81.1 Moderately increased 8.7 7.4 12.3 Increased 4.2 3.3 6.6 Financial status Never 58.3 59.7 54.4 Sometimes 32.8 32.0 34.9 Often 8.9 8.3 10.7 School climate rating Very high 6.4 6.9 5.1 High 40.2 42.0 35.8 Moderate 44.3 43.2 47.5 Low 5.3 4.7 6.9 Very low 3.8 3.3 4.8 Mean in total population Mean Mean Social media problems Total score 1.17 1.11 1.30 Gaming problems Total score 0.68 0.66 0.71 Bullying behaviour Person score* 0.04 0.03 0.04 Eating and physical activity Person score* 0.50 0.49 0.53 Alcohol use Person score* 0.22 0.19 0.27 Substance use Person score* 0.13 0.11 0.16 Sexual behaviour Person score* 0.10 0.08 0.14 Perceived safety Person score* 0.10 0.10 0.12 Adverse life events Person score* 0.13 0.12 0.15 Family relations Person score* 0.27 0.27 0.28 * For clarity, person scores were standardized to a 0 - 1 scale (with: tidyLPA). Table 2 – Health behavioural-, psychosocial- and socio-economic characteristics in the four profiles identified by LPA (page 11) A. Psychosocial problems group %n, n = 596 B. Less problem-practical educated group %n, n = 1019 C. Less problem-theoretical educated group %n, n = 3576 D. Risk taking behaviour group %n, n = 698 Statistical significant differences Gender Male 43.0 53.7 51.3 58.7 D > B, C > A Female 57.0 46.3 48.7 41.3 A > B, C > D Level of education Practical pre-vocational education 29.4 100.0 0.0 29.2 B > A, D > C Theoretical pre-vocational education 26.5 0.0 29.5 16.3 A, C > D > B Senior general secondary education 26.0 0.0 32.2 38.5 D > C > A > B Pre-university education 18.1 0.0 38.3 7.0 C > A, D > B Physical health No medication use or chronic illness 54.2 68.4 72.0 58.2 B, C > A, D Chronic illness or uses medication 15.3 14.2 12.1 17.8 D > A, B > C Chronic illness and uses medication 30.5 17.4 15.9 24.1 A, D > B, C Mental health Normal 0.0 99.4 99.6 78.5 B, C > D > A Moderately increased 66.4 0.6 0.4 14.5 A > D > B, C Increased 33.6 0.0 0.0 7.0 A > D > B,C Financial status Never 38.3 65.9 61.7 46.4 B, C > D > A Sometimes 41.1 30.0 31.2 38.5 A, D > B, C Often 20.6 4.0 7.1 15.0 A, D > C > B School climate rating Very high 2.7 8.4 6.6 6.0 B, C, D > A High 18.8 40.5 44.9 33.8 B, C > D > A Moderate 54.7 42.0 42.8 46.3 A > B, C, D Low 13.4 4.6 3.7 7.7 A > D > B, C Very low 10.4 4.4 2.0 6.2 A > B, D > C Mean Mean Mean Mean Social media problems Total score 1.77 1.04 1.08 1.31 A > D > B,C Gaming problems Total score 1.14 0.62 0.61 0.67 A > D, B, C Bullying behaviour Person score* 0.17 0.01 -0.04 0.05 A > D, B, C Eating and physical activity Person score* 0.16 0.19 -0.09 0.06 A, B, D > C Alcohol use Person score* 0.12 -0.28 -0.04 0.69 D > A > C > B Substance use Person score* 0.09 -0.16 -0.07 0.51 D > A > C > B Sexual behaviour Person score* -0.19 -0.19 -0.19 1.78 D > A, B, C Perceived safety Person score* 0.15 -0.02 -0.04 0.09 A, D > B, C Adverse life events Person score* 0.39 -0.08 -0.08 0.20 A > D > B, C Family relations Person score* 0.22 -0.10 -0.03 0.12 A, D > B, C Table 3 - School absenteeism in the four identified profiles (page 12) Mean in total population mean A. Psychosocial problems group mean B. Less problem-vocational educated group mean C. Less problem-theoretical educated group mean D. Risk taking behaviour group mean Statistical significant differences Truancy (hours in past 4 weeks) 0.66 (SD 3.54) 1.25 (SD 3.86) 0.79 (SD 5.39) 0.41 (SD 1.88) 1.29 (SD 5.64) D, A, B > C Sickness absence (days in past 4 weeks) 1.18 (SD 2.21) 1.64 (SD 2.62) 1.28 (SD 2.44) 0.97 (SD 1.90) 1.70 (SD 2.78) D, A, B > C %n, n = 5889 %n, n = 596 %n, n = 1019 %n, n = 3576 %n, n = 698 Total school absenteeism B > C ≥16h in past 4 weeks 27.0 37.7 28.1 23.0 37.5 A, D > B > C Table 4 - Reasons for absenteeism in the four identified profiles (page 13) Reason for absenteeism Mean in total population %n, n = 5889 A. Psychosocial problems group %n, n = 596 B. Less problem-practical educated group %n, n = 1019 C. Less problem-theoretical educated group %n, n = 3576 D. Risk-taking behaviour group %n, n = 698 Motivation 70.6 73.2 65.5 71.1 72.0 Tiredness 1.5 1.5 2.3 1.3 1.7 Stress 2.5 2.8 2.7 2.6 2.0 School 44.0 52.1 46.0 41.8 43.4 Psychosocial 3.7 8.3 3.9 2.5 3.7 School subject 23.8 32.1 26.5 21.9 20.0 Unsafe school climate 1.3 3.5 1.2 0.8 1.7 Learning difficulties 11.8 21.3 12.5 9.0 14.1 Other 0.7 1.0 0.8 0.7 0.2 Additional Declarations No competing interests reported. Supplementary Files appendices.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4243252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292218915,"identity":"3a243ae5-09e2-4569-bb76-cd887ca4287f","order_by":0,"name":"Lindi Korpelshoek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACPmYGBomEAyAmcwOQzWDAD2InFODWwobQwgjRItkA0mKARwsQSzAgazEAc/BpYec9eOPBGZt8c/bG5s8FFTbGxudXJ354YMAgzy92AIfD+JItEm6kWe7sOdgmPeNMmpnZjbebJYAOM5w5OwGHFh4ziYQPhw0MbiS2MfO2HbYxu3F2A0hLgsFtQlruP2z+zNv238Z4xtnNPwhruQGyhbFBmrftgJkBf+82QrYYWyScSTOw7Elsk+Y5k2wscYN3m0WCgQROv/DznzG8+eOYjYE5++HDn3kq7Az7+89uvvmjwkaeXxq7FjhARIQEWKUEfuWoWvgPEFY9CkbBKBgFIwoAAJXzXRSXa9iZAAAAAElFTkSuQmCC","orcid":"","institution":"Leiden University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Lindi","middleName":"","lastName":"Korpelshoek","suffix":""},{"id":292218916,"identity":"b5a4190d-ee2f-426f-aa06-4488718e7ad4","order_by":1,"name":"Rikkert Martijn Lans","email":"","orcid":"","institution":"Leiden University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Rikkert","middleName":"Martijn","lastName":"Lans","suffix":""},{"id":292218917,"identity":"969291fe-f8d1-4306-8aaa-f721602cc471","order_by":2,"name":"Mathilde Rosalie Crone","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"Rosalie","lastName":"Crone","suffix":""}],"badges":[],"createdAt":"2024-04-09 16:28:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4243252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4243252/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55251613,"identity":"605ca5d3-5cfb-4362-bb9f-e481ce26585c","added_by":"auto","created_at":"2024-04-24 17:41:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOutcomes of the LPA: differences between the four identified profiles \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e(page 11)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4243252/v1/063341f89afd6bdfdecc6f0b.png"},{"id":90965747,"identity":"7ce4900f-12f2-4ce3-a6f0-4a6a7c7e7ec2","added_by":"auto","created_at":"2025-09-10 06:32:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4243252/v1/1612a65d-d4a8-4174-9842-a2f295368a6e.pdf"},{"id":55251614,"identity":"6fea0f83-528b-43b5-ac14-ce70cc302846","added_by":"auto","created_at":"2024-04-24 17:41:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29717,"visible":true,"origin":"","legend":"","description":"","filename":"appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-4243252/v1/e107bb7cc837629fc5456edf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relation of school absenteeism with adolescent profiles of health behavioural-, psychosocial- and socio-economic characteristics: a cross-sectional study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSchool absenteeism is a common problem both in the Netherlands as worldwide, in the Netherlands defined as \u0026lsquo;more than 16 hours of absence in the past four weeks\u0026rsquo;\u003csup\u003e1\u003c/sup\u003e. Approximately 5% of all Dutch adolescents absented more than 16 hours in 4 weeks during the schoolyear of 2018-2019\u003csup\u003e2\u003c/sup\u003e. The term \u0026lsquo;School absenteeism\u0026rsquo; comprises several terms, including \u0026lsquo;school refusal\u0026rsquo;, \u0026lsquo;truancy\u0026rsquo;, and \u0026lsquo;sickness absence\u0026rsquo;. School refusal refers to a child-motivated refusal to attend school\u003csup\u003e3\u003c/sup\u003e. Truancy refers to a school absence due to inexcusable reasons, e.g. to pursue stimuli such as illegal activities, delinquency or gaming\u003csup\u003e5\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral risk factors are associated with school absenteeism, but school absenteeism itself is also a predictor for various problems, such as adult psychosocial problems, poor school performance and increased risk for school drop-out\u003csup\u003e6\u003c/sup\u003e. This aspect emphasises the importance of identifying adolescents who are at risk in time. However, identifying adolescents at risk for absenteeism and elucidating the risk factors for school absenteeism are complex due to several wide-range risk factors associated with each of them\u003csup\u003e3,7,8\u003c/sup\u003e. Berends and Van Diest\u003csup\u003e7\u003c/sup\u003e provided an overview of risk and protective factors for school absenteeism and categorised them into the following five domains: the adolescent, home environment, peers, school and context. The domain adolescent spans risk factors like physical health problems, mental health difficulties and unhealthy behaviours\u003csup\u003e9\u003c/sup\u003e. The domain home environment spans risk factors related to poverty and family conflicts\u003csup\u003e9\u003c/sup\u003e. The domain peers spans peer-related risk factors, which influence increases especially during middle school\u003csup\u003e7\u003c/sup\u003e. The domain school environment spans factors like boredom with school, poor school climate, low-quality teachers and inadequate education\u003csup\u003e7\u003c/sup\u003e. Finally, the domain context spans risk factors like low socio-economic status and neighbourhood characteristics\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSchool absenteeism is also a predictor of various health problems. Previous research found that (mental) health problems, social behaviours and health behaviours, including school absenteeism, tend to cluster in adolescence\u003csup\u003e10,11,12,13\u003c/sup\u003e. Results of these studies indicate that risk-seeking behaviours tend to cluster together, including alcohol use, drug use, smoking, risky sexual behaviour and delinquent behaviour\u003csup\u003e14,15,16,17\u003c/sup\u003e and that sedentary behaviours is another group of behaviours that tend to cluster together, such as low physical activity, poor nutritional intake and increased daily screen time\u003csup\u003e18,19\u003c/sup\u003e. However, these studies have only limitedly addressed factors in the contextual and school domains. Moreover, they often lack information on internet and social media use, which is considered a new concern for adolescent health. Therefore, evidence from old clustering studies may not be representative, as they include TV and computer screen time instead of current internet behaviour\u003csup\u003e20\u003c/sup\u003e. Only recently have studies also included social media and smartphone use. A systematic review\u003csup\u003e21\u003c/sup\u003e showed that all social media domains\u0026mdash;time spent, activity, investment and addiction\u0026mdash;are associated with depression, anxiety and psychological distress, which emphasises the importance of updating the knowledge about current internet-related behaviour. In conclusion, previous studies on clustering found that the co-occurrence of social, behavioural and (mental) health problems is associated with increasingly adverse psychosocial and physical health outcomes. However, how these findings relate to school absenteeism remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent public health interventions usually target a single type of behaviour in isolation\u003csup\u003e7\u003c/sup\u003e. In addition, effectiveness of these interventions varies depending on the co-occurring problems of the adolescent\u003csup\u003e22\u003c/sup\u003e. Interventions focusing on multiple domains have been proven to be most effective for adolescents with multiple health problems\u003csup\u003e23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, the literature on school absenteeism provides information about individual risk factors and less about the clustering of risk factors. Studies that inspect the clustering of problems in adolescence mostly include only a limited number of factors and do not examine its relation to school absenteeism. The aim of the current study is therefore to explore whether specific adolescent profiles can be identified based on a broad range of characteristics and to assess how these profiles are related to school absenteeism. The research questions in this study are 1) \u0026lsquo;Can profiles be identified within adolescents based on characteristics related to the adolescent, family environment and school environment?\u0026rsquo;, 2) \u0026lsquo;What is the incidence of school absenteeism in the identified profiles?\u0026rsquo;, and 3) \u0026lsquo;Are there differences in reasons for absenteeism between the identified profiles?\u0026rsquo;.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study concerns a cross-sectional observational study using routinely collected data from preventive youth health care services. The legal review board of the municipal health centre approved the use of their database for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data concern self-report questionnaires administered in school years 2016–2017 and 2019-2020 in the municipality of The Hague. As part of a pre-existing preventive health check (JongerenConsult Check Up), students receive an invitation to take the JongerenConsult questionnaire in either class 3 or 4 of secondary school. This questionnaire aims to monitor and improve the health of adolescents.\u0026nbsp;Students in grade 3 or 4 are usually 14-16 years old.\u003c/p\u003e\n\u003cp\u003eFor the check-up, adolescents are invited by e-mail to complete the questionnaire. They are informed about the aim and procedure of the check-up, both by e-mail and during class hours. Adolescents have to give informed consent. Parents are also informed by e-mail and have the option to object to participation. Adolescents complete the questionnaire during class hours. Based on their answers in the questionnaire, adolescents with an increased risk of adverse (mental) health outcomes receive an invitation for a consult with the school nurse. Adolescents are informed that the questionnaire data and consultations are strictly confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample is a census of all schools for secondary education situated in the area The Hague. The Hague is the third largest city in the Netherlands with an ethnically diverse population (Dutch [44%], other Western nationality [19%], non-Western nationality [37%])\u003csup\u003e24\u003c/sup\u003e. A total of 5,889 adolescents aged 14-16 completed the JongerenConsult questionnaire and were included in the analyses. The average age was 15 years. Gender was evenly distributed, and 48.2% were girls. Within the participant group, all represents all levels of education. Schools for special education were not invited to participate. The Dutch secondary education begins after elementary education, typically at the age of 12. Based on teacher advice and the results of the Cito test (final year assessment of elementary school), a choice is made for one of the following types of secondary education: 1) preparatory vocational secondary education (VMBO), having a practical subtype (VMBO-b/k) and a theoretical subtype (VMBO-t), 2) senior general secondary education (HAVO) and 3) university preparatory education (VWO)\u003csup\u003e25\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire we used is comparable to the E-MOVO\u003csup\u003e26\u003c/sup\u003e, which is widely used in secondary schools during the adolescent preventive health check in the Netherlands. It includes questions about school, physical and mental health, health-related behaviours, and social and family contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables for analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome\u003c/em\u003e. School absenteeism was measured as the sum of truancy (hours) and sickness absenteeism (days). The typical duration of a school day is 8 hours. The outcome school absenteeism was categorised as follows: ‘0’ refers to ‘less than 16 hours of absence in the past four weeks’ and ‘1’ refers to ’16 or more hours of absence in the past four weeks’. This definition is based on Dutch governmental guidelines\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis included 16 variables that together provided information on a mix of observable personal characteristics and latent measurements of the various adolescent domains. The variables were: gender (1 = ‘male’ and 2 = ‘female’), level of education (1 = ‘practical pre-vocational education’, 2 = ‘theoretical pre-vocational education, 3 = ‘senior general secondary education’ and 4 = ‘pre-university education’), eating and physical activity (4 items), alcohol use (3 items), substance use (3 items), sexual behaviour (2 items), physical health (2 items), mental health (measured by the total score on the Strengths and Difficulties Questionnaire [SDQ]\u003csup\u003e27\u003c/sup\u003e), bullying behaviour (2 items), social media problems (7 questions), gaming problems (7 questions), family relations (4 items), adverse life events (3 items), financial problems (1 item), perceived safety (2 items) and school rating (1 item). The detailed information about the items is listed below in Appendix A.\u003c/p\u003e\n\u003cp\u003eAmong all item responses, on average, 0.4% of the answers were missing. A total of 3,809 and 2,080 students completed the 2016–2017 and 2019–2020 questionnaires, respectively. Missing data was imputed using the Multivariate Imputation via Chained Equations (mice) package in Rstudio. Missing data was imputed for the following variables: bullying, alcohol use, substance use, sexual behaviour, eating and physical activity, family relations, and adverse life events. The datasets before and after imputing were analysed to examine whether the outcomes differed. No significant differences were found.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReliability and scaling\u003c/em\u003e. Item response theory (IRT) was applied to construct the scales for all constructs that contained more than two questions. This method was performed in the Rstudio using the R package mirt\u003csup\u003e28\u003c/sup\u003e. The IRT analysis computes a weighted person score for each participant based on the item responses to all questions of a specific construct. In the estimation of the person scores, IRT gives items more weight than other items depending on the item difficulty (which is derived from the frequency of occurrence) and item discrimination (which is derived from the item-scale correlation). IRT was applied to the following measures: bullying, eating and physical activity, alcohol use, substance use, sexual behaviour, safety, adverse life events, and family relations. A-parameters were examined for each item, and each measure included items with similar a-parameters. Trace plots were examined to examine the distribution of chosen answer options to verify whether the categorised answer options were properly categorised.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReasons for school absenteeism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore reasons for school absenteeism, we analysed the question ‘What is the most important reason for you to be absent from school?’. The participant had choice of 11 answer options and could answer the open answer box. Both multiple choice and open questions were analysed, and the answers were categorised as follows: stress, unsafe school environment, motivation, school-related, subject-related, psychosocial, learning difficulties and tiredness. Analysis of the answers was conducted separately by two researchers in order to minimalize researcher bias in the response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirst research question\u003c/em\u003e. A latent profile analysis (LPA) was performed in the Rstudio using the tidyLPA package. The indicator variables used for identifying profiles were gender, level of education, physical health, mental health, bullying, problems caused by social media, problems caused by gaming, eating and physical activity, alcohol use, substance use, sexual behaviour, family relations, adverse life events, safety, financial status and school rating. The LPA fits a number of models equal to the number of variables. The package MClust was used to derive information about model fit, including Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the model entropy. In general, lower AIC and BIC values indicate that the model fits the data better\u003csup\u003e29\u003c/sup\u003e. Entropy indicates how accurate the individuals were assigned to the latent profiles. Entropy varies from 0 to 1, where a higher value indicates a more accurate classification. AIC, BIC and entropy were used to determine the most accurate model.\u003c/p\u003e\n\u003cp\u003eIn the next step, the variation of students assigned to different profiles was examined. All values were standardised to a 0–1 scale by the ‘poms’ function in tidyLPA. The package ggplot2 was used to plot Graph 1, which shows how the groups scored on the various variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSecond research question\u003c/em\u003e. Independent sample t-tests and chi-square tests were used to compare the identified profiles regarding total school absenteeism, truancy, sickness absence and reasons for absenteeism. The alpha level was set to 0.01. Furthermore, Bonferroni corrections were used to correct for chance inflation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 1 here\u003c/strong\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAs stated in Table 1, adolescents who were absent for more than 16 hours were more often girls and reported more often chronic illness and medication use, poor eating and exercise behaviour, a moderately increased or increased SDQ score, problems caused by social media and gaming, alcohol and substance use, risky sexual behaviour, adverse life events, perceiving an unsafe environment, poor financial status and an adverse school climate compared with adolescents who were absent for less than 16 hours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent profile analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA model with four profiles was estimated as the most adequate, with an entropy of 0.999. Appendix C shows the fit indicators of the first five models in the LPA. The identified four profiles were distributed as follows: profile A (10.1%), profile B (17.3%), profile C (60.7%) and profile D (11.9%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo groups had overall high scores on most variables, and two groups showed low scores on most variables. The first group with increased absenteeism rates was defined as the psychosocial problem group (profile A), which is characterised by mental health problems (seen as a substantial increased SDQ score), more problems caused by social media and gaming, low school climate rating, more adverse life events, poorer family relations and more chronic illness and medication use than the other groups. The second group with high scores was defined as the risk-taking behaviour group (profile D), as these adolescents reported more often using alcohol and other substances and engaging in risky sexual behaviour than the other groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe two groups with low scores on health and psychosocial behaviours and problems were divided into profile B, which only presents students following practical vocational education (less problem-vocational educated group), and profile C, which represents students following a theoretical education (less problem-theoretical educated group). Besides differences in education, the theoretical-educated group had better eating and physical activity habits and more alcohol and substance use than the vocational-educated group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 2 here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 1 here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbsenteeism outcomes in the four identified profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReported truancy and sickness absence were on average 0.66 hours (SD 3.54) and 1.18 days (SD 2.21) in the past 4 weeks, respectively. Truancy and sickness absence rates were significantly higher in both problem groups. In addition, the vocational-educated group reported more truancy and sickness than the less problem theoretical-educated group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe percentage of students who were absent for more than 16 hours in the past 4 weeks was 27% in the total study population; however, these school absenteeism percentages varied markedly between the four profiles. Total absenteeism was 37.7% in the psychosocial problems group, 37.5% in the risk-taking behaviour group, 28.1% in the less problem-vocational educated group and 23.0% in the less problem-theoretical educated group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 3 here\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReasons for absenteeism\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most reported reasons for absenteeism were motivation (70.6%), school-related reasons (44.0%), school subject-related reasons (23.8%) and learning difficulties (11.8%). Commonly reported in the open answer box were \u0026lsquo;feeling tired\u0026rsquo; or \u0026lsquo;lessons begin too early\u0026rsquo;, although both were not represented in the 11 answer options. School absenteeism reasons were comparable in both problem groups, although the psychosocial problems group overall reported more reasons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 4 here\u003c/strong\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to explore whether adolescent profiles could be distinguished based on health behavioural-, psychosocial- and socio-economic characteristics, and how these profiles relate to school absenteeism. We identified two different adolescent profiles with more problems and two with fewer problems. In the psychosocial problem group (A), adolescents are characterised by an increased risk of mental health problems, chronic illness or medication use, problems caused by social media and gaming, financial problems, adverse life events, poor family relations and low school climate ratings. The risk-taking behaviour group (D) was in particular more likely to report risky sexual behaviour, use of alcohol and other substances, and also to a lesser extent, chronic illness or medication use and financial and family-relational problems. Total school absenteeism rates were above average in these two problem groups. The other groups (B, C) were characterised by fewer problems and had lower school absenteeism rates. The vocational-educated group (B) showed lower scores on eating and exercise behaviour compared with the theoretical-educated group (C), whereas the latter reported more alcohol and substance use than group B. The most reported reasons for absenteeism were motivation, school-related reasons, school subject-related reasons and learning difficulties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings were in line with earlier studies; however, an exact comparison was not possible due to variations in the methods and in the included variables. The co-occurrence of alcohol and substance use, risky sexual behaviour and male gender found in profile D is, for example, often discussed in the literature\u003csup\u003e13,16,17,30,31\u003c/sup\u003e. In addition, the co-occurrence of sedentary behaviour\u003csup\u003e32\u003c/sup\u003e, screen time\u003csup\u003e33\u003c/sup\u003e, poor mental wellbeing and female gender\u003csup\u003e34\u003c/sup\u003e is previously described in the literature, which was consistent with the characteristics in profile A. Furthermore, consistent with the literature, we found that poor physical health is intertwined with problems in other domains\u003csup\u003e30,35\u003c/sup\u003e. Both our problem profiles (A, D) reported more chronic illness or medication use, in combination with increased risk of mental health problems in particular for profile A, risk-taking behaviour in particular for profile D, and socioeconomic problems for both profiles. In addition to other studies, we also included information about social media use, adverse life events and family relations, which were all clearly part of the psychosocial problem profile and, to a lesser extent, also of the risk-taking behaviour profile.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen comparing the two profiles with fewer problems (B, C), the most important distinguishing characteristics between these two groups were educational level, physical activity and eating habits, and alcohol and substance use; the lower educated profile reported unhealthier physical activity and eating habits, whereas the higher educated profile reported more use of alcohol and other substances. These findings indicate that educational level is associated with poor eating habits and physical activity, which is consistent with other studies\u003csup\u003e36,37,38,39\u003c/sup\u003e. However, the frequent alcohol and substance use in the higher educational group is surprising because other studies on Dutch adolescents showed that these behaviours are more prevalent among lower educated students\u003csup\u003e40\u003c/sup\u003e. One hypothesis for this difference could be linked to the ethnicity ratios in The Hague being deviant from the Dutch average: 43% of adolescents in The Hague have a non-Western ethnicity compared with a Dutch average of 18%\u003csup\u003e41\u003c/sup\u003e. Adolescents with a non-Western ethnicity more often show unhealthy eating and physical activity habits but less often use alcohol and other substances, often with strict rules regarding these substances\u003csup\u003e40\u003c/sup\u003e. In addition, they also more frequently follow lower education\u003csup\u003e41\u003c/sup\u003e, which might explain this discrepancy with other studies. However, due to privacy reasons, we were not able to use the data regarding ethnicity and could not test this hypothesis.\u003c/p\u003e\n\u003cp\u003eAll included variables were associated with higher absenteeism rates, supporting current evidence of reviews\u003csup\u003e1,2\u003c/sup\u003e. Substance use and externalizing behaviour, roughly corresponding to our profile D, are the most predictive for school absenteeism\u003csup\u003e13\u003c/sup\u003e. However, our results also showed that a combination of problems is an indicator of increased school absenteeism rates. For example, unhealthy behaviours were more prevalent among absenting adolescents, but this factor was only applicable for adolescents in the psychosocial and risk-taking profiles, who more often reported problems on multiple domains. This knowledge could explain why interventions focusing on a single behaviour are less effective\u003csup\u003e9\u003c/sup\u003e. The lowest absenteeism profile C also had considerably low standard deviations for school absenteeism, suggesting a more homogeneous group; this result indicates that being a healthy adolescent with less psychosocial-, family- and socioeconomic problems and following higher educational levels is associated with consistent low absenteeism rates. Nevertheless, the lower educated group had lower absenteeism rates when having fewer problems in other domains. Although on the pre-university level, the profiles with higher absenteeism rates were somewhat less prevalent, and they were observed in all educational levels, suggesting that targeted preventive interventions should be integrated in all educational levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations of this study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA strength of this study is the broad range of included variables, especially the information on current social media behaviour, family relations and school characteristics, which valuably improve the current knowledge. Our participating group is unique, which is not only large but also considerably represents the complete The Hague adolescent population, as all adolescents were invited to participate in this JongerenConsult questionnaire, which is used as the first step in a standard preventive health consultation. In addition, using IRT in calculating person scores strongly strengthens this study, ensuring that the distinctions found are clear and valid. Furthermore, the entropy of the four-profile model was very high (0.99), indicating that the LPA identified four distinct profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLimitations included the JongerenConsult questionnaire, which was not specifically designed for research purposes. Furthermore, due to our urban population, we cannot guarantee our results to be representative of average Dutch adolescents. Furthermore, we used quantitative data to explore reasons for absenteeism, but to clearly understand a complex and multifactorial problem this could best be supplied with qualitative research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecall bias might have occurred, as all information were self-reported. However, an internal municipal health service study compared our questionnaire with an anonymous similar questionnaire and found that in the non-anonymous questionnaire, psychosocial problems were underreported, whereas alcohol use and school absenteeism were overreported. However, we expect that its impact on our study is limited, as this bias is present in all groups.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe total absenteeism prevalence in this study, which is 27%, indicates that school absenteeism forms an important concern for schools that cannot be ignored. The identified profiles provide extended information about adolescents at risk for school absenteeism. The identified problem profiles were associated with higher absenteeism rates and were present through all educational levels. The clustering of health, psychosocial, family and socioeconomic problems implicates that school absenteeism requires a comprehensive approach, focusing not only on individual risk factors but also on the interaction of risk factors in an adolescent\u0026rsquo;s life. Furthermore, the growing use of the internet and social media produces a new concern for adolescent health, and its co-occurrence with family problems may be an concerning combination that warrants further exploration.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC: Akaike information criterion\u003c/p\u003e\n\u003cp\u003eBIC: Bayesian information criterion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIRT: item response theory\u003c/p\u003e\n\u003cp\u003eLPA: latent profile analysis\u003c/p\u003e\n\u003cp\u003eMICE: multivariate imputation via chained equations\u003c/p\u003e\n\u003cp\u003eProfile A: Psychosocial problems group\u003c/p\u003e\n\u003cp\u003eProfile B: Less problems practical educated group\u003c/p\u003e\n\u003cp\u003eProfile C: Less problems theoretical educated group\u003c/p\u003e\n\u003cp\u003eProfile D: Behavioural problems group\u003c/p\u003e\n\u003cp\u003eSDQ: strength and difficulties questionnaire\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author of this study received permission from the legal department of Municipal Health service of The Hague for use of their database with anonymous school health check questionnaire information for research and publication purposes. The data was gathered by the Municipal Health Service of The Hague.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the nature of the research, due to legal restrictions supporting data is not available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLK, MRC and RML designed the study and performed the data analyses. LK made the tables and figures. All authors participated in the interpretation of the findings and finalizing the paper. LK wrote the first draft of the article. MRC and RML critically revised all draft versions. The final draft was approved by all authors. All authors share equal accountability for the paper and provided final approval for the submitted manuscript. LK is the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial acknowledgment goes to Y. Turfboer for assistance in obtaining and utilizing the anonymous school health check questionnaire database of the Municipal Health service of The Hague.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors of this study declare they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDienst Uitvoering Onderwijs. Soorten verzuim. 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Behavioral classes related to physical activity and sedentary behavior on the evaluation of health and mental outcomes among Brazilian adolescents. PLoS One. 2020;15(6):e0234374. DOI:10.1371/journal.pone.0234374.\u003c/li\u003e\n\u003cli\u003ePlotnikoff RC, Karunamuni N, Spence JC, et al. Chronic disease-related lifestyle risk factors in a sample of Canadian adolescents. J Adolesc Health. 2009;44:606\u0026ndash;609. DOI:10.1016/j.jadohealth.2008.11.004\u003c/li\u003e\n\u003cli\u003eMoor I, Rathmann K, Stronks K, et al. Psychosocial and behavioural factors in the explanation of socioeconomic inequalities in adolescent health: a multilevel analysis in 28 European and North American countries. J Epidemiol Community Health. 2014;68:912\u0026ndash;921. DOI:10.1136/jech-2014-203933\u003c/li\u003e\n\u003cli\u003eHuurre T, Aro H, Rahkonen O, Komulainen E. Health, lifestyle, family and school factors in adolescence: predicting adult educational level. Educ Res. 2006;48:1,41-53. DOI:10.1080/00131880500498438.\u003c/li\u003e\n\u003cli\u003eKarvonen S, Rimpel\u0026auml; A. Socio-regional context as a determinant of adolescents\u0026apos; health behaviour in Finland. Soc Sci Med. 1996;43(10):1467-1474. DOI:10.1016/0277-9536(96)00044-5.\u003c/li\u003e\n\u003cli\u003eDe Vries H, van \u0026apos;t Riet J, Spigt M, Metsemakers J, van den Akker M, Vermunt JK, Kremers S. Clusters of lifestyle behaviors: results from the Dutch SMILE study. Prev Med. 2008;46(3):203-208. DOI:10.1016/j.ypmed.2007.08.005. \u003c/li\u003e\n\u003cli\u003eStevens G, Van Dorsselaer S, Boer M, et al. HBSC 2017: Gezondheid en welzijn van jongeren in Nederland. The Netherlands: Trimbos Instituut, 2018. \u003c/li\u003e\n\u003cli\u003eCentraal Bureau Statistiek. VO; leerlingen, onderwijssoort, leerjaar, migratieachtergrond, generatie. Available at: StatLine - Vo; leerlingen, onderwijssoort, leerjaar, migratieachtergrond, generatie (cbs.nl). Accessed April 2024. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Adolescent characteristics sorted by school absenteeism \u003cem\u003e(page 9)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"728\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e%n in total population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;16h Absenteeism\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e%n, n = 4244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;16h Absenteeism\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e%n, n = 1574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e54.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePractical pre-vocational education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eTheoretical pre-vocational education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eSenior general secondary \u0026nbsp;education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePre-university education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e26.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e29.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eNo medication use or chronic illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e70.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eChronic illness or uses medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eChronic illness and uses medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e89.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e81.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eModerately increased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFinancial status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e59.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eSchool climate rating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.17032967032967%\" valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.65289256198347%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.305785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895604395604394%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.96153846153846%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.78021978021978%\" valign=\"top\"\u003e\n \u003cp\u003eMean in total population\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.994505494505493%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.36813186813187%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eSocial media problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eTotal score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eGaming problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eTotal score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eBullying behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eEating and physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eSubstance use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eSexual behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerceived safety\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eAdverse life events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.15680880330124%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eFamily relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.235213204951858%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003ePerson score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.1939477303989%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.019257221458048%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.39477303988996%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* For clarity, person scores were standardized to a 0 - 1 scale (with: tidyLPA).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash; Health behavioural-, psychosocial- and socio-economic characteristics in the four profiles identified by LPA \u003cem\u003e(page 11)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"749\" style=\"margin-right: calc(6%); width: 94%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003eA. Psychosocial problems group\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e%n, n = 596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003eB. Less problem-practical educated group\u003c/p\u003e\n \u003cp\u003e%n, n = 1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003eC. Less problem-theoretical educated group\u003c/p\u003e\n \u003cp\u003e%n, n = 3576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003eD. Risk taking behaviour group\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e%n, n = 698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical\u003c/strong\u003e \u003cstrong\u003esignificant\u003c/strong\u003e differences\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e51.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eD \u0026gt; B, C \u0026gt; A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e48.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; B, C \u0026gt; D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" rowspan=\"4\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003ePractical pre-vocational education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eB \u0026gt; A, D \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eTheoretical pre-vocational education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, C \u0026gt; D \u0026gt; B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eSenior general secondary \u0026nbsp;education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eD \u0026gt; C \u0026gt; A \u0026gt; B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003ePre-university education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eC \u0026gt; A, D \u0026gt; B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" rowspan=\"3\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eNo medication use or chronic illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e68.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eB, C \u0026nbsp;\u0026gt; A, D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eChronic illness or uses medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eD \u0026gt; A, B \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eChronic illness and uses medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eMental health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e99.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eB, C \u0026gt; D \u0026gt; A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eModerately increased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e66.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D \u0026gt; B,C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" rowspan=\"3\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eFinancial status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e65.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eB, C \u0026gt; D \u0026gt; A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, D \u0026gt; C \u0026gt; B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" rowspan=\"5\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eSchool climate rating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eB, C, D \u0026gt; A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e44.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eB, C \u0026gt; D \u0026gt; A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eModerate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; B, C, D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.982456140350877%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2046783625731%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.058479532163743%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.5906432748538%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; B, D \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.823529411764707%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.44385026737968%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.631016042780749%\" valign=\"top\" style=\"width: 7.1019%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.631016042780749%\" colspan=\"\" valign=\"top\" style=\"width: 7.1835%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.631016042780749%\" colspan=\"\" valign=\"top\" style=\"width: 7.1019%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.631016042780749%\" colspan=\"\" valign=\"top\" style=\"width: 7.1835%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" colspan=\"\" valign=\"top\" style=\"width: 7.7549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eSocial media problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003eTotal score\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D \u0026gt; B,C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eGaming problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003eTotal score\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D, B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eBullying behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D, B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eEating and physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, B, D \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eAlcohol use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eD \u0026gt; A \u0026gt; C \u0026gt; B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eSubstance use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eD \u0026gt; A \u0026gt; C \u0026gt; B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eSexual behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eD \u0026gt; A, B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003ePerceived safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eAdverse life events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA \u0026gt; D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.8%\" valign=\"top\" style=\"width: 5.3876%;\"\u003e\n \u003cp\u003eFamily relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.4%\" valign=\"top\" style=\"width: 11.0201%;\"\u003e\n \u003cp\u003e\u003cem\u003ePerson score*\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.4896%;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.733333333333333%\" colspan=\"\" valign=\"top\" style=\"width: 8.408%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.866666666666667%\" colspan=\"\" valign=\"top\" style=\"width: 8.7345%;\"\u003e\n \u003cp\u003eA, D \u0026gt; B, C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 - School absenteeism in the four identified profiles \u003cem\u003e(page 12)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.903743315508022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.903743315508022%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eMean in total population\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003eA. Psychosocial problems group\u003c/p\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eB. Less problem-vocational educated group\u003c/p\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eC. Less problem-theoretical educated group\u003c/p\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eD. Risk taking behaviour group\u003c/p\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical\u003c/strong\u003e \u003cstrong\u003esignificant\u003c/strong\u003e differences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.44385026737968%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eTruancy\u003c/p\u003e\n \u003cp\u003e(hours in past 4 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.66 (SD 3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e1.25 (SD 3.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 (SD 5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e0.41 (SD 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e1.29 (SD 5.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eD, A, B \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.44385026737968%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eSickness absence\u003c/p\u003e\n \u003cp\u003e(days in past 4 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.18 (SD 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e1.64 (SD 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e1.28 (SD 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (SD 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e1.70 (SD 2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eD, A, B \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.903743315508022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.903743315508022%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e%n, n = 5889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e%n, n = 596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e%n, n = 1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e%n, n = 3576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e%n, n = 698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.903743315508022%\" valign=\"top\"\u003e\n \u003cp\u003eTotal school absenteeism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.903743315508022%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;16h in past 4 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e73.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e71.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eA, D \u0026gt; B \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.903743315508022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.903743315508022%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;16h in past 4 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.032085561497325%\" valign=\"top\"\u003e\n \u003cp\u003eA, D \u0026gt; B \u0026gt; C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 - Reasons for absenteeism in the four identified profiles \u003cem\u003e(page 13)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eReason for absenteeism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003eMean in total population\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e%n, n = 5889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003eA. Psychosocial problems group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e%n, n = 596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003eB. Less problem-practical educated group\u003c/p\u003e\n \u003cp\u003e%n, n = 1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003eC. Less problem-theoretical educated group\u003c/p\u003e\n \u003cp\u003e%n, n = 3576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003eD. Risk-taking behaviour group\u003c/p\u003e\n \u003cp\u003e%n, n = 698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eMotivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eTiredness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eSchool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003ePsychosocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eSchool subject\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eUnsafe school climate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eLearning difficulties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.317991631799163%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"school absenteeism, adolescents, psychosocial behaviour, substance use, health behaviour, family","lastPublishedDoi":"10.21203/rs.3.rs-4243252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4243252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND: \u003c/strong\u003eSeveral wide-range risk factors are associated with school absenteeism. Complexities arise from the multitude and clustering of these risk factors, challenging recognisability of adolescents at risk. This study aimed to identify usable adolescent profiles based on health behavioural-, psychosocial- and socio-demographic characteristics. School absenteeism outcomes of these profiles were compared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eA total of 5,889 Dutch secondary school students completed a self-report questionnaire on (1) physical- and mental health, (2) health-related behaviours, and (3) school-, social- and family environments. Profiles of adolescents with similar characteristics were identified using a latent profile analysis. School absenteeism rates and reasons for absenteeism were compared among the identified profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eFour profiles were identified: profile A (10.1%), profile B (17.3%), profile C (60.7%) and profile D (11.9%). \u0026nbsp;Two profiles (A, D) showed increased absenteeism risks, each displaying a combination of somatic, mental, social, and family-related problems. Profile A was characterized by psychosocial problems, while profile D was characterized by risk-taking behaviors. Both profile A and D were present across all educational levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS: \u003c/strong\u003eThe clustering of health behavioural, psychosocial, family and socioeconomic problems implies that school absenteeism requires a comprehensive approach, focusing not only on individual risk factors but also on the interaction of risk factors in an adolescent’s life. Knowledge about our identified profiles can be used to better recognize adolescents at risk and to tailor current interventions in practice, in order to decrease the burden caused by school absenteeism.\u003c/p\u003e","manuscriptTitle":"The relation of school absenteeism with adolescent profiles of health behavioural-, psychosocial- and socio-economic characteristics: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 17:41:10","doi":"10.21203/rs.3.rs-4243252/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea08e4a6-eae0-4dfd-9ab1-446ce31e7857","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T06:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 17:41:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4243252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4243252","identity":"rs-4243252","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}