The Course and Remission of Suicidality from Ages 11 to 29: Findings from a Dutch Population-Based Longitudinal Cohort Study

The Course and Remission of Suicidality from Ages 11 to 29: Findings from a Dutch Population-Based Longitudinal Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Course and Remission of Suicidality from Ages 11 to 29: Findings from a Dutch Population-Based Longitudinal Cohort Study Mandy Gijzen, Bertus Jeronimus, Diana Bergen, Albertine J Oldehinkel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9314087/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Suicide is a leading cause of death among adolescents and emerging adults. Suicidality is a multidimensional construct that includes suicidal ideation (SI), self-harm (SH), suicide attempts, and suicide completion, without a fixed or linear progression between these elements. We examined the course of suicidality (SI/SH) over adolescence and young adulthood (age 11–29) and correlates of natural remission and recovery over these 18 years, using the Dutch population-based longitudinal TRAcking Individuals Lives Survey (TRAILS). We examined whether perceived belongingness and social support are associated with recovery from suicidality, for example, as predicted by the Interpersonal Theory of Suicide. Methods TRAILS followed 2229 adolescents aged 11 (SD = 0.6, 49% women) and we studied to age 29, with 7 waves 2–3 years apart. The prevalence of suicidality and changes therein were quantified using the Adolescent/Youth Self-report (Achenbach system), and we examined potential psychosocial and demographic correlates, such as self-esteem, self-efficacy, temperament/personality, and cognitive style, socioeconomic status, and gender. Results The correlation between SI/SH increased from early adolescence (age 11 r = ~ .33) to stabilize over mid adolescence (ages 15–29, r = ~ .50). SI was somewhat more common in youth (4.4–8.3%) than SH was (1.9%-5.3%) and peaked earlier in time (SI, age 11) than SH (age 14), and suicidality declined gradually over adolescence and young adulthood. The low base-rate of SI/SH in our prospective general population sample proved a significant barrier to investigate the specific mechanisms of natural recovery in suicidality, as luckily > 90% of youth reported no suicidality. Conclusion Protective and recovery factors for SI and SH remain difficult to identify, and apparently require general population studies of twenty thousand adolescents or more in the Western world. Complementary risk samples and time series or process approaches, interviews, and psychological autopsy studies can help identify prevention and protective factors to reduce suicidality among youth. Suicidality Remission Protective factors Epidemiology Longitudinal cohort Introduction Suicidality is a multidimensional construct that encompasses suicidal thoughts (i.e., suicidal ideation), plans, gestures, self-harm, attempts, and suicide completion, without implying a fixed or linear progression between these elements (Gijzen et al., 2026). Suicide is a leading cause of death among adolescents (ages 12–18) and emerging adults (ages 18–30), and on the rise, despite increasing prevention efforts (World Health Organization, 2025). The prevalence of youth suicidal ideation ranges from 14% to 23%, while suicide attempts and self-inflicted injuries occur in approximately 5% to 16% of adolescents (such as self-poisoning or cutting/burning) (Brunner et al., 2014; Carballo et al., 2020; Van Meter et al., 2023). One fifth (~ 21%) of all European individuals reported a dead wish at some point during their lives (see Castillejos et al., 2021). Suicidality describes “the risk of suicide, usually indicated by suicidal ideation or intent, especially as evident in the presence of a well-elaborated suicidal plan” (American Psychological Association, 2018). Suicidal behavior includes self-harm, suicide attempts, and suicide completion. Suicidality typically emerges between ages twelve and thirty but shows great individual variation in how thoughts and behaviors evolve over time (Cipriano et al., 2017; Goldston et al., 2016; Knipe et al., 2022; Naghavi, 2019). Suicidality is often experienced as a transient episode, although a third of young people follow more persistent or severe trajectories (Barrocas et al., 2015; Erausquin et al., 2019), which may require intensive professional care. Suicidality researchers typically focus on the emergence, escalation and persistence of suicidal ideation and self-harm, and risk factors, such as mental health problems, trauma, or impulsivity (Carballo et al., 2020). Far less is known about decreases in suicidality (ideation or self-harming) over time, and the processes that underlie recovery, which are crucial for positive and strengths-based approaches to suicide prevention and intervention (Gijzen et al., 2026). Prospective studies on the onset and persistence of suicidality indicate that ~ 70% of youth show declines in suicidal ideation and self-harm over time (i.e. “remission”), thus suicidality often disappears in 12–24 months (e.g., Gunnell et al., 2004; Teismann et al., 2016; ten Have et al., 2009, see Borges et al., 2008) for longer follow-ups). Half of the adolescents who have attempted suicide exhibited an adolescent-limited course with reduced risk of adult suicide attempts (Geoffroy et al., 2022). Natural remission of suicidality is thus common, and among youth who reported self-harm thoughts at baseline, only 31% reported them six months later (Russell et al., 2020). To balance the current emphasis on the emergence and persistence of suicidality (e.g., Franklin et al., 2017; prospectively Geoffroy et al., 2021; Kerr et al., 2008; Mars et al., 2019; Moran et al., 2012), we need prospective population-based cohort studies that quantify rates and identify predictors of symptom reduction, sustained remission, and recovery from suicidality over time, as they are lacking (Gijzen et al., 2026). Remission and recovery and predictive factors One community-based UK cohort study identified distinct trajectories of suicidality (i.e., remission, persistence, and late-onset), but the predictors distinguishing these groups, especially for remission, remain poorly understood (Mulholland et al., 2023). Remission from suicidality has been associated with social support, purpose in life, and positive mental health (Herzog et al., 2022; Teismann et al., 2016), but the patterns prove neither clear nor consistent (Rath et al., 2021). An US population study reported that remission from suicidal ideation and attempts was more likely among women and people with higher income, who were married or single (vs. divorced), and those without sleep problems, childhood abuse, or recent psychiatric diagnoses (Fuller-Thomson et al., 2019). Social relationship quality (warmth or confidentiality) and quantity and perceived social support (especially from parents) and family financial status predicted cessation of suicidal ideation and self-injury (vs. persistence) among adolescents and emerging adults (e.g., Rajhvajn Bulat et al., 2024; Whitlock et al., 2015), and better emotion regulation strategies and more life satisfaction. Recovery from suicidality proved affected by various factors, often interpersonal (e.g., social or practical support), intrapersonal (e.g., psychological wellbeing i.e., coping, self-acceptance, autonomy, personal development), contextual (e.g., demographics [gender, education, partner, child], societal awareness and stigma), and clinical or treatment-related factors (Gijzen et al., 2026; Grimmond et al., 2019; Lakeman & FitzGerald, 2008; Lewin et al., 2024; Sokol et al., 2022). Evidently both micro-level determinants (psychosocial and clinical processes) and meso-level contextual conditions (socioeconomic position, family stability, access to resources) are important in shaping recovery trajectories. A multilevel system perspective in keeping with the exposome framework in which health outcomes such as recovery from suicidality is the outcome of a dynamic and cumulative interplay of biological, chemical, physical, and social exposures embedded within individuals’ lived environments (Gudi-Mindermann et al., 2023; Ungar et al., 2013). Population-based research that systematically examines factors associated with decreases in suicidality to full remission during the transition from late adolescence into adulthood is scarce (see for a review (Gijzen et al., 2026), especially in the large prospective cohort studies (Gijzen et al., 2026) that underlie many prevention strategies. Individual-level changes over time can help identify specific mechanisms of natural recovery, and such insights that are often missed when focusing solely on clinical or high-risk samples. Studies of trajectory modeling to identify subgroups based on patterns of suicidality do not directly quantify how many individuals experience a reduction in suicidal thoughts or behaviors between assessment waves. We examine the course of suicidality and the potential correlates of remission and recovery across adolescents and young adulthood in the Dutch population-based longitudinal TRAcking Individuals Lives Survey (TRAILS), and focus on natural remission rates during the transition from adolescence to adulthood; which we defined as a reduction in self-reported suicidal ideation and/or self-harming behaviors between consecutive measurement waves (see Table 1 ). Our remission is a tangible and observable outcome, but we do not imply full psychosocial recovery, which typically includes broader improvements in well-being, social functioning, or resilience (see Gijzen et al., 2026; Grimmond et al., 2019; Lakeman & FitzGerald, 2008; Lewin et al., 2024; Sokol et al., 2022). The present study We examine (a) the prevalence and changes in suicidal ideation (SI) and self-harm (SH) behavior between ages 11–29 in the general population and (b) try to identify factors that associated with these improvements in SI/SH or recovery between waves (see Table 1 ). Because the Interpersonal Theory of Suicide (Joiner, 2005) theorizes that changes in perceived belongingness and social support play a key role in suicidality, we focus on potential psychosocial and demographic correlates, including self-esteem, self-efficacy, socioeconomic status, and gender. Method Study design and population This study was preregistered at the Open Science Framework ( https://doi.org/10.17605/OSF.IO/Y8ETQ ) and used data from the TRacking Adolescents’ Individual Lives Survey (TRAILS), a large-scale prospective cohort study of Dutch adolescents tracked from adolescence to young adulthood (de Winter et al., 2005; Oldehinkel et al., 2015). In 2001, participants (N = 2229; mean age = 11.1, SD = 0.6; 51% female) were recruited via community registers and primary schools in the northern Netherlands (~ 76% baseline response rate; de Winter et al., 2005). The TRAILS cohort was examined approximately every 2–3 years and we used data from the first seven waves ( T 1−7 ). Retention, age and gender can be found in Table 1 . Both youth and their parents completed questionnaires. Teachers were also asked to complete a brief questionnaire for each TRAILS child in their class. Comprehensive information about the cohort is provided elsewhere (Oldehinkel et al., 2015; de Winter et al., 2005). Our deviations from our pre-registered paper strategy are provided in Supplementary Table S1 . Table 1 Youth suicidal ideation (SI) and self-harm (SH): Their correlation ( r ) and prevalences (%) in the TRAILS population cohort Wave Years Age (Mean, SD) and Sex (%) Measure Prevalence (%) Changes (+/-) per wave (%) M SD ♀ N r SI SH - SH + - SI + T 1 2000-01 11.1 .56 49% 2229 YSR .33 8.3 4.1 T 2 2003-04 13.6 .53 51% 2148 YSR .49 7.6 5.3 3.0 92.6 4.4 5.7 88.5 5.5 T 3 2006-07 16.3 .71 57% 1818 YSR .49 5.8 4.6 4.3 92.2 3.4 6.0 90.3 3.6 T 4 2009-10 19.1 .60 56% 1880 ASR .51 4.4 2.6 3.7 94.6 1.7 4.5 92.3 3.4 T 5 2012-13 22.3 .65 58% 1781 ASR .53 4.4 1.9 2.2 96.5 1.3 3.5 93.5 3.1 T 6 2016-17 25.7 .60 61% 1616 ASR .49 6.1 2.3 1.5 96.6 1.8 2.6 93.3 4.1 T 7 2019-20 28.9 .60 66% 1231 ASR .49 5.0 2.2 1.7 96.9 1.4 4.4 92.3 3.4 Note. All correlations were significant at p <.001 (two-tailed). ♀= proportion women. ASR = Adult self report (126 items). M = mean. N = number of participants available at each wave. SD = Standard Deviation. r = correlation coefficient between SI and SH. SH = Self-Harm behavior (YSR item 18 and ASR item 15). SI = Suicidal Ideation (YSR item 91 and ASR item 76). T 1 = baseline wave at time point 1. YSR = Youth Self Report (112 items; Achenbach & Rescola, 2001). We show the proportion of participants who were stable or increased (+) or decreased (-) in symptoms (S x ) of suicidality between measurement waves (e.g., T 2−7 ), and details on these changes are provided in Supplementary Tables S2 (SH) and S3 (SI). Measures Our primary outcomes were suicidal ideation (SI) and self-harm (SH) and a priori determined putative recovery factors comprised individual factors, health-related factors, and social factors. Suicidality Suicidal ideation (SI) and self-harm (SH) were measured with 2 items per wave between ages 11 and 29 (see Table 1 ), each rated on a three-point scale ranging from 0 = not at all true to 2 = very often or very true. A higher score indicates more SI/SH. The Youth Self Report (YSR) and Adult Self Report (ASR) have been shown to be reliable and valid tools for assessing adolescents aged 11 to 18, based on research involving participants from 23 different countries and various gender and age groups (Ivanova et al., 2007). We measured suicidal ideation with items 91 of the YSR and item 76 of the ASR and self-harm behavior with items 18 of the YSR and item 15 of the ASR. Self-harm behavior as measured with the YSR and ASR includes harming oneself (regardless of intention) and attempted suicide. For both prevalence estimation and logistic regression analyses, the three-point scale was dichotomized: scores of 0 were coded as absence of SI/SH, and scores of 1 or 2 were coded as presence of SI/SH. Consequently, prevalence (%) and regression outcomes reflect the presence versus absence of at least mild suicidal ideation or self-harm. Sociodemographic factors We measured sex and age at each wave via self or parent report (see Table 2 ), and participants reported about their sexual orientation (straight, gay, or bisexual) during waves T 4–6 . We based socioeconomic status (SES) on parental education, profession, and income. Parents reported on their own and their child's religiosity, from expression of faith in daily life to perceived centrality of faith in their child's existence. Social wellbeing and social skills Sources of wellbeing. The Social Production Function (SPF; Ormel et al., 1997) instrument was used to capture how adolescents thought they were perceived by their parents, friends, classmates and teachers, which was used to quantify how need satisfaction creates subjective well-being (following SPF theory). The items are rated on a 5-point Likert scale from never to always with higher scores indicating more social well-being (Nieboer et al., 2005). The SPF questionnaire was used to assess the satisfaction of affection, behavioural confirmation, status, stimulation, and comfort needs, from the participants’ perspective of their parents, teachers, classmates and peers. School belongingness. Three questions on adolescent school belongingness were developed by TRAILS and completed by a teacher (acceptable reliability T 2 : α = 0.55; T 3 : α = .50). Social skills. The Social Skills Rating System (Gresham & Elliott, 1990) was completed by both teacher and parents. It assesses three subscales: Cooperation, assertion, and self-control. Only assertion was used for the present study. Items are rated as 0 = not true , 1 = somewhat or sometimes true , or 2 = very often or often true . One example item read: ‘My child starts conversations by himself instead of waiting for others to initiate communication’. Reliability was good at T1: Teacher, α = 0.88; Parent, α = 0.75). Prosocial behavior. The prosocial behavior questionnaire (Tremblay et al., 1992; Weir & Duveen, 1981) comprised 11 items to assess whether an adolescent has the tendency to react prosocially to various situations (e.g., “Asks an outsider to join in during a game?”) and was completed by teachers and showed high reliability ( T 2 : α = 0.92). Future expectations Participants indicated the likelihood of various life events occurring in ten years (e.g., employment status, partnership, parenthood) on a 5-point scale from 'Very low' to 'Very high'. We only included the items regarding future part-time and full-time job expectations. Individual traits and coping Fear of negative social evaluation. Fear of Negative Social Evaluation was assessed using 5 items based on the scale used by (Tops et al., 2008). The total score is calculated by the sum of all items. Items are scored on a 4-point scale (not at all true, hardly true, moderately true, exactly true), with good reliability (α = 0.79). Self-efficacy. The Generalized Self-Efficacy (GSE: Schwarzer & Jerusalem, 1995) was used to measure self-efficacy. The total score is calculated by finding the sum of all items. Items are scored on a 4-point scale (not at all true, hardly true, moderately true, exactly true). For the total GSE, the total score ranges between ten and 40, with a higher score indicating more self-efficacy. Only a subset of five out of the ten of the full scale were used. The reliability was good (α = 0.80). Coping. The Adolescent Cognitive Style Questionnaire (ACSQ; Hankin & Abramson, 2002) was used to measure cognitive coping styles, which consists of ten items. It inquires how the participant would react to two different situations (not having a partner and not doing a good job at work or school). The ACSQ measures five different cognitive styles, namely ASCQ: Situation attributed to internal causes (two items), Situation attributed to stable causes (two items), Situation attributed to global causes (two items), Negative inferences for consequences (two items), Negative inference for self (two items). The reliability of the total scale with ten items was high (α = 0.83) Family environment Parental rearing styles. Parental rearing styles were measured with the Egna Minnen Betraffende Uppfostran (EMBU-C: Markus et al., 2003) which consists of three subscales: overprotection, emotional warmth, and rejection. Overprotection includes fearfulness, anxiety for the child’s safety, guilt induction, and intrusiveness: “When you come home, you have to tell your parents what you have been doing”. Emotional warmth refers to giving special attention, praise, unconditional love, and supportive affection: “Your parents show that they love you”. Rejection involves hostility, punishment, derogation, and blaming of the child: “Your parents criticize you in front of others”. The T 4 list contains only 8 relevant items related to emotional warmth and rejection. Reliability was good at T 1 (18 items, α = 0.91 (father) α = 0.91 (mother)) and at T 4 (Warmth Father: 4 items, α = 0.88; Warmth Mother: 4 items, α = 0.86; Rejection Father: 4 items, α = 0.70; Rejection Mother: 4 items, α = 0.67). Parental reactions. Participants were asked to complete questions regarding angry outbursts, problem solving, and guilt inducing reactions from parents. Only the problem-solving items were used for this study, which measured how parents reacted to disagreements, e.g. ‘Your father/mother really wants to understand why you did what you did’. The reliability of these items was acceptable at T 3 (father: α = .81; mother: α = .77). Parental monitoring. Participants were also asked about how their parents talked to them proactively (mother, father, caregiver), and showed interest in their life (parental solicitation), and how the child shared aspects of their life with the parent proactively (child disclosure). Reliability was at T 3 acceptable (disclosure father: α = .67; disclosure mother: α = .72; sollicitation father: α = .76; sollicitation mother: α = .72). Example items read: ‘If you've been out late at night, do you spontaneously tell your father/mother what you've done when you get home?’ (disclosure), and ‘Does your father/mother start a conversation with you about things that happened during a normal school day?’ (sollicitation). Parental knowledge. Parental knowledge questions regarding friends, time spending and substance use (smoking, alcohol, weed) were developed by TRAILS and one example item reads: ‘How much does your father/mother know about who your friends are?’. It consists of 8 items (4 for mother and 4 for father). Reliability was acceptable for both mother (α = .72) and father (α = .78) items. Family functioning. Family functioning was measured with the Family Assessment Device (FAD: Miller et al., 1985) completed by parents at waves T 1 - 6 and comprised 12 statements regarding how the family functions (e.g., “We don’t get along well in our family”). Each statement can be rated using the following four answer categories; “strongly agree”, “agree”, “disagree”, and “strongly disagree”. A higher score indicates a more negative family functioning. Reliability was good ( T 1−6 : α = .85-.88). Romantic relationships Participants were asked whether they were in a romantic relationship with a partner. Work Participants were asked whether they had a job. If they indicated they did, they were then asked about job satisfaction using one item from the Copenhagen Psychosocial Questionnaire (COPSOQ: Kristensen et al., 2005), and work engagement using the Utrecht Work Engagement Scale (UWES: Schaufeli et al., 2006). The UWES comprised three items, which had a good reliability (α = 0.86). One sample item reads: ‘At work I am bursting with energy’. Each item is rated as ‘Sporadically’, ‘Occasionally’, ‘Regularly’, ‘Often’, ‘Very often’, ‘Always’ ‘I don't know’, or ‘Not applicable’. Statistical analyses Logistic regression was used to examine which factors predicted a decrease in suicidal ideation or self-harm between each wave. 1 Logistic regression has fewer distributional assumptions and can handle correlated predictors more robustly than discriminant analyses can (see Table S1 ), especially when combined with multiple imputation to handle missing data under the Missing at Random (MAR) assumption (Rubin, 1987; Sterne et al., 2009). The use of multiple imputation allows for more accurate parameter estimation and inference compared to complete case analysis or simple imputation methods (White et al., 2011). Moreover, logistic regression directly models the probability of outcome categories, facilitating clear interpretation of odds ratios for individual predictors across waves (Hosmer Jr et al., 2013). Recent methodological recommendations favor logistic regression in longitudinal studies with complex missingness and interrelated predictors (Sterne et al., 2009; White et al., 2011). Multiple imputation was performed separately for each wave and outcome variable using the fully conditional specification (FCS) method implemented in IBM SPSS Statistics. This method is well-suited to handle different missingness patterns and variable sets across timepoints, and it has been shown to produce unbiased parameter estimates under the missing at random (MAR) assumption (Buuren, 2018; Rubin, 1987). For each wave ( T 2−7 ) and outcome (improvement in suicidal ideation [SI] and self-harm [SH]), 20 imputed datasets were generated. Improvement was operationalized using change scores, calculated by subtracting the score at the previous wave from the score at the current wave (e.g., T 2 _SI_change = SI at T 2 – SI at T 1 ). To ensure that improvement was meaningful, only participants with a score of 1 or 2 (i.e., elevated SI or SH) at the prior wave were considered. Within this subgroup, improvement was defined as a subsequent decrease in score (i.e., a negative change value). Participants with a score of 0 at the previous wave or missing data at either timepoint were excluded from the analysis. These filtered change scores were recoded into a binary outcome variable, where 1 indicated improvement (decline in SI or SH) and 0 indicated no change (stability). Participants with increased scores (worsening) or missing values were not included in the regression models. These binary improvement indicators served as the dependent variables in the logistic regressions. The imputation models included all independent variables relevant to the specific wave and outcome, which were selected based on theoretical frameworks and prior empirical evidence. Importantly, predictors included variables measured at previous waves to capture temporal dynamics and potential lagged effects (Sterne et al., 2009). Sex and socio-economic status were included as control variables for each wave. Logistic regression analyses were subsequently performed on each imputed dataset to examine predictors of improvement in SI and SH at each wave. Estimates from the 20 imputed datasets were combined using Rubin’s rules to obtain pooled odds ratios (ORs) and 95% confidence intervals (CIs; (Rubin, 1987). This wave-specific approach allowed for tailored modeling of predictors at discrete timepoints while appropriately handling missing data without bias introduced by irrelevant variables. Statistical significance was evaluated at p < .05. All analyses were conducted using IBM SPSS Statistics version 28. To account for the hierarchical structure and conceptual clustering of predictors, blockwise logistic regression analyses were performed (see Table 2 ). For each wave ( T 2−7 ) and outcome (SI and SH improvement), predictors were grouped into theoretically coherent blocks, with control variables (sex and SES) entered in the first block (A), and subsequent blocks (B-F) based on developmental relevance and proximity to the outcome (e.g., mental health and wellbeing, school context, work support), which allowed us to evaluate the incremental predictive value of each domain above and beyond more distal factors. This blockwise approach was chosen to facilitate interpretability, limit multicollinearity, and explore whether more proximal or developmentally salient factors contributed uniquely to improvement in SI/SH. The block structure varied across waves in accordance with the availability of measures and developmental timing. A full overview of block composition per wave is provided in Table 2 , an approach that enabled us to examine changes in psychosocial predictors of decreases over time, while accounting for both stability and developmental shifts in relevant domains. Table 2 Overview of Thematic Predictor Blocks per Wave ( T 2 – T 7 ). Wave & Block Thematic Group R Variables SI SH OR p OR p T 2 A Control variables P Sex 1.14 .76 2.24 .21 P SES 1.08 .81 0.71 .51 B Mental health & wellbeing S Warmth 1.82 .25 2.40 .23 S Group belongingness 0.81 .51 0.66 .43 P Assertion 0.92 .90 0.38 .39 P FAD 2.61 .11 .065 .66 C Source of wellbeing S Mother 1.20 .68 1.00 .99 S Father, 0.97 .92 .056 .31 S Teacher 1.08 .82 0.99 .98 S Friends and classmates 1.56 .28 2.02 .31 T 3 A Control variables P Sex 1.58 .36 3.60 .18 P SES 0.87 .66 1.84 .23 B Family context P FAD 1.09 .86 1.22 .77 C Parental knowledge S Mother 1.98 .40 0.89 .91 S Father 0.49 .47 1.23 .83 D School context T School wellbeing 0.46 .20 1.22 .78 T Prosocial beh. 1.53 .40 1.21 .81 E Source of wellbeing S Teacher 1.09 .84 1.06 .90 S classmates 1.17 .66 1.24 .63 T 4 A Control variables P Sex 0.42 .18 1.10 .94 P SES 0.88 .78 1.02 .98 B Child wellbeing P Child is doing better than 2 yrs ago 0.39 .37 2.87 .51 C Parental monitoring & reactions S Child disclosure, father 0.52 .35 0.32 .39 S Child disclosure, mother 1.04 .95 1.36 .81 S Paternal sollicitation 3.95 .06 2.18 .54 S Maternal sollicitation 0.52 .38 0.17 .18 D Family context P FAD 0.66 .60 4.23 .27 E School context T School wellbeing 0.75 .72 1.20 .85 F Source of wellbeing T Teacher 1.00 .99 3.05 .26 T Classmates 0.95 .92 1.35 .75 T 5 A Control variables P Sex 0.50 .39 * P SES 0.34 .07 B Family context S EMBU-C 1.79 .22 P FAD 1.64 .61 C Romantic relationship S Yes/no 0.81 .82 D Cognitive style & self-efficacy S ACSQ 1.07 .85 S FNSE 0.59 .41 S GSE 1.65 .45 E Job expectation in 10 yrs S Fulltime 1.76 .17 S Parttime 0.87 .87 F Religiosity P Religosity 1.37 .49 T 6 A Control variables P Sex 1.55 .48 0.23 .25 P SES 0.70 .45 0.86 .86 B Romantic relationship S Yes/no 0.56 .38 1.83 .66 C Family functioning P FAD 1.08 .92 0.87 .90 T 7 A Control variables P Sex 0.94 .89 1.15 .90 P SES 0.91 .83 1.18 .78 B Romantic relationship S Yes/no 0.92 .90 1.26 .83 C Work support S COPSOQ 0.90 .90 1.26 .86 S UWES 1.04 .94 0.86 .82 D Family functioning P FAD 0.79 .75 0.64 .74 Note . Baseline ( T 1 ). * For T5 SH there were too few people (n = 1) classified as stable as such logistic regression was not possible. ACSQ = Adolescent Cognitive Style Questionnaire. COPSOQ = Copenhagen Psychosocial Questionnaire. EMBU-C = Egna Minnen Beträffande Uppfostran Questionnaire for Children. FAD = Family Assessment Device. FNSE = Fear of Negative Social Evaluation. GSE = General Self-efficacy Scale. P = Parent. R = report. S = self-reported. SES = Socioeconomic Status. Sex (1 = male). Yrs = years. UWES = Utrecht Work Engagement Scale. Results The prevalence rates, mean scores, and correlations for suicidal ideation (SI) and self-harm (SH) among the 2229 participants between ages 11 and 29 across the seven waves of the TRAILS cohort ( T 1−7 ) are provided in Table 1 . SI and SH were measured with single items (YSR ages 11–16, ASR ages 19–29) and the prevalence (%) reflects the proportion of participants endorsing at least mild SI or SH, based on dichotomized scores (0 = absence; 1 or 2 = presence on the original three-point scale). Across adolescence and into young adulthood, SI prevalence ranged from 4.4% to 8.3%, with a peak around age 11 and gradual decline over adolescence and emerging adulthood (see Table 1 ), and a modest increase observed at age 26 (6.1%). SH showed a similar, though overall lower, pattern: prevalence ranged from 1.9% to 5.3%, peaking around age 14, and declining more steeply thereafter. This suggests that both SI and SH are most common in early to mid-adolescence, with decreasing frequency as participants transition into adulthood. Despite these age-related declines, the correlation between SI and SH remained consistently moderate and significant across all waves ( r = .33 to .53, p < .001), indicating that youth who reported suicidal ideation were also more likely to report self-harming behavior, regardless of age. Suicidality over time In the original non-imputed dataset, a small proportion of young people experienced suicidality at each time point (see Table 1 ); suicidal ideation (4.4–8.3%) was more common than self-harming behaviors (1.9–5.3%), and both were reported more during early adolescence than late adolescence. We note that most adolescents reported surprisingly stable levels of both suicidal ideation (88.5–93.5%) and self-harm (92.2–96.6%) over time, and only a small proportion of participants reported a decrease in SI or SH over time (ranging from 0.1% to 5.6%), fairly comparable to the small proportion who reported an increase (0.1% to 5.1%). Factors related to decreases in suicidality To examine predictors of improvement in SI and SH, logistic regression analyses were conducted for each time point. Since model fit statistics such as the omnibus test of model coefficients, Nagelkerke R², and classification accuracy are not pooled across imputed datasets in SPSS, these values were calculated from the logistic regression models based on the original (non-imputed) dataset and should be interpreted as indicative only. Supplementary Table S4 presents the number of cases included and missing data per wave for the original datasets. At age 13.6 ( T 2 , see Table 1 ) the final pooled model for suicidal ideation including all predictor blocks (see Table 2 ) did not identify predictors of improvement of both SI and SH. This lack of results could reflect that only eight participants were in the stable-high group. At all other waves, for both the suicidal ideation and self-harm model, we also did not find any significant predictors for improvement. We think the absence of results reflects that too few people were classified as stable-high in suicidality (i.e., persistent suicidality) across two waves for our models, although this was good news for the participants. Discussion Main findings We studied the developmental trajectories of suicidal ideation (SI) and self-harm (SH) over adolescence and emerging adulthood and focused on reductions in suicidality, to examine which factors associated with improvement, such as perceived group belongingness and social sources of wellbeing, to examine the Interpersonal Theory of Suicide (Joiner, 2005). Our TRAILS sample of 2,229 adolescents age 11 from the general Dutch population was followed to age 29, which allowed us to examine their suicidality over 7 measurement waves (YSR/ASR) and the rates of natural recovery (Table 1 ), and (b) factors that associate with recovery from SI/SH (Table 2 ). Our results show that SI/SH were rare among children, adolescents, and emerging adults, as fortunately ~ 92% did not experience suicidality, and a small percentage of youth experiences either an increase or decrease in suicidality over time (0.1% to 5.6%). The low prevalence of SI/SH and the few participants who improved or recovered in SI/SH precluded robust associations with external factors (low base-rate; (Serdar et al., 2021), thus even with our large population panel of 2,229 adolescents, we proved to be underpowered. The prevalence of SI/SH in our sample (measured 2001–2018) was largely in line with Dutch reports over 2021 (~ 92.4%, past 3 months; (National Institute for Public Health and the Environment [Rijksinstituut voor de Volksgezondheid en Milieu], 2025), although more recently a small rise has been observed during the COVID-19 pandemic (2022-24, to ~ 13–17% of Dutch adolescents). The international SI prevalence of ~ 16.4% (Van Meter et al., 2023) and for SH of 16.9% (Gillies et al., 2018) indicates that the prevalence in TRAILS was fortunately slightly lower. Youth SI/SH reports were relatively stable across waves (Table 1 ), which appears to be inconsistent with ecological momentary assessment (EMA) and intensive longitudinal studies that demonstrated that SI and associated risk states fluctuate markedly within individuals over hours or days, including sudden shifts from low-to-high-risk states without intermediate planning (Czyz et al., 2019; Czyz et al., 2022; Hallensleben et al., 2019; Kleiman et al., 2017). TRAILS used two self-report items from the Achenbach System of Empirically Based Assessment (ASEBA) to evaluate suicidality (item #18 “deliberately harms self or attempts suicide” and #91 “talks about killing self”), among other emotional, behavioral, and social problems, and these items were able to distinguish youth with a history of suicidal behavior from peers (Van Meter et al., 2018). Such single-item, interval-based self-report measures were designed to study prevalence/incidence in representative population samples with long follow-up, to stratify scores by sociodemographic factors and other between-person factors, but may insufficiently capture short-term variability and transition dynamics, and have limited utility for prospective risk prediction or for detecting nuanced reductions in suicidality across time. Such daily dynamics may also prove unimportant for long-term outcomes, however, and future studies could shed more light on this paradox. The three-month window used in ASR/YSR items likely captures low-base-rate outcomes better than short EMA windows because of its longer reference period, but it may also be more vulnerable to recall and reporting biases due to the extended retrospective window and temporal averaging (e.g., Stone et al., 2023). Note that suicide risk scales have demonstrated limited clinical utility for predicting future suicide attempts (Franklin et al., 2017; Runeson et al., 2017), and national clinical guidelines caution against using such tools to predict suicide risk or determine treatment decisions (NICE, 2022). Coarse measurement can underestimate relationships with proximal predictors, and panel-wave timing may miss the relevant risk window. EMA studies allow researchers to study within-person fluctuations in suicidality and process-oriented models, but are not a golden solution, as they make only sense in high-risk samples, while in general population samples (like ours) the low prevalence of SI/SH (low base rate) and reduced spread also limits statistical power to detect associations with external factors, as has frequently been noted in EMA studies of suicidal thoughts and behaviors due to their low base rate (e.g., Bernanke et al., 2017; Kleiman & Nock, 2018). Additionally, a recent EMA study with suicidal patients in the Netherlands found a completion rate of only 17.6% (Nuij et al., 2022), and EMA assessments are particularly missed during moments of stress (Myroniuk et al., 2026). Recently researchers proposed there may be rapid-fluctuating stress-responsive and more persistent non-stress-responsive suicidal phenotypes, with their own risk factors and pathophysiology (Bernanke et al., 2017), which could suggest different methodologies tap into different types of suicidality. Our results showed that most youth reported suicidality between ages 11–16 (TRAILS T 1 - T 3 ), in line with research that indicates that suicidal ideation generally rises between ages 11 and 17 among US youth (Nock et al., 2013). A German study indicated that suicidal behavior begins to rise around age 10 (< 1%), increases gradually to 2.2% by age 12, and then escalates sharply, reaching 13.5% until the age of 20 (Voss et al., 2019). A Dutch study found that suicidal ideation did not persist for most youth between ages 13 and 16 years (van Vuuren et al., 2020). Importantly, low individual-level persistence does not necessarily imply declining prevalence, as increases may occur if new cases emerge during the same period. Furthermore, the global onset of the first mental disorder is before age 14 in one-third of individuals, and half before age 18 (Solmi et al., 2022), in keeping with the aforementioned rise in suicidality over adolescence. Trajectories of suicidality research has mostly focused on risk factors, but positive self-perceptions and self-esteem (Bakken et al., 2025; Zhu et al., 2019), life satisfaction (Zhu et al., 2019), academic achievement and school connectedness/wellbeing (Bakken et al., 2025; Zhu et al., 2019), family and friend support (Adrian et al., 2016), social adjustment (Goldston et al., 2016) could be identified as potential factors related to decreases in suicidality over time during adolescence. Our analyses could not identify any protective factors or combination of factors that could accurately predict decreases in suicidality over time within the TRAILS cohort, which highlights the complexity of identifying such protective or recovery factors (Gijzen et al., 2026). It may not only be difficult to pinpoint protective factors, but also to develop or sustain them over time, particularly in high-risk groups. Confounding by baseline severity of suicidality or related mental health problems can affect our results, e.g., people with high self-esteem tend to report less SI/SH (Buecker et al., 2025), which leaves them less room for improvement over time. Changes in suicidality may partly reflect regression to the mean rather than true improvement (Barnett et al., 2004), and apparently demand more suitable study design and statistical methods. Our study may have also been limited by instruments that were better suited to detect risk rather than protective factors, especially subtle or indirect effects. We demonstrate that the low base-rate of SI/SH in our prospective general population samples of > 2.200 adolescents like TRAILS poses a significant barrier to investigate the specific mechanisms of natural recovery in suicidality. Our conclusion that the low base rate of suicidal events necessitates massive population samples echoes a warning by the American National Research Council (American National Research Council, 2002), in hindsight (20/20). Stable correlation estimates with recovery factors require > 250 adolescents who recover (improve) in SI/SH (see Schönbrodt & Perugini, 2013), which, at our prevalence rates ( P = 0.015, via ~ 1.5-5% in Table 1 , and 10–20% drop-out), means about twenty thousand adolescents. Which would require TRAILS to be nine times larger, and to include three times the number of adolescents aged 11 that were available in the Northern three provinces of the Netherlands, and ~ 15% of all adolescents aged 12–18 that lived there (Statistics Netherlands, 2026). Hence, suicidality research in general may focus more on how specific relational processes can alleviate physiological stress responses and buffer youth against the development of suicidality, such as conflict resolution and supportive parenting strategies, as such family interventions can reduce risk of suicidality long-term (e.g., Connell et al., 2023) over a decade), and proves effective across various populations of youth. Strengths and limitations One key strength of this study is the high data quality of the TRAILS dataset, which ensures reliable and valid assessments of key variables. The large sample size and rigorous data collection methods enhance the generalizability and robustness of the findings. However, several limitations must be acknowledged. First, as discussed, suicidality is relatively rare, which has limited our statistical power and the ability to detect meaningful effects. Additionally, suicidal ideation and self-harm were measured using a single-item assessment (Table 1 ), which, while common in research, may not fully capture the complexity of these constructs, and their potential to fluctuate rapidly within-people over time. Our lack of results at the group-level also stimulates us to study individual-level dynamics, processes, and predictors, as factors may not be protective for all youth. Furthermore, cohort studies like TRAILS tend to emphasize risk factors associated with adverse outcomes, as identifying predictors of psychopathology and suicidality is critical for early intervention. The measurement of protective factors is often comparatively poor, however, which may play a crucial role in resilience and recovery research. A more balanced approach incorporating both risk and protective factors could enhance the predictive accuracy of suicide research and inform more effective prevention strategies. Recommendations for future research A recovery-focused approach in a very large longitudinal cohort study could involve not only tracking established protective factors but also incorporating measures of recovery dynamics that go beyond mere symptom reduction. These could include variables that assess post-traumatic growth (e.g., perceived personal growth, meaning-making), shifts in identity (e.g., self-concept, self-worth), or the development of positive future outlooks (e.g., hope for the future, life satisfaction). Moreover, the study could focus on resilience in the face of adversity by measuring adaptability and the ability to manage setbacks, as well as access to adaptive coping mechanisms and how these evolve over time. Conclusion It remains difficult to find which factors are related to a decrease in either suicidal ideation or self-harm. Few participants experienced a decrease in suicidality, which makes statistical analyses difficult to perform. EMA or qualitative approaches may be better suited for looking at rare and highly fluctuating phenomena such as suicidality. Declarations Clinical trial number: Not applicable. Ethics approval and consent to participate The TRAILS protocol was approved by the Central Committee on Research Involving Human subjects (CCMO): NL67411.042.18. Informed consent was obtained of all participating youth and their parents after the nature of the study had been fully explained. Consent for publication Not applicable. Data availability The data that support the findings of this study are available from the TRAILS-consortium (www.trails.nl), but restrictions apply to the availability of these data, which were used with specific permission for the current study and are not publicly available. Due to privacy restrictions and ethical regulations, the raw data are not publicly available. Access to the data can be requested through the respective study secretariats, subject to approval by their scientific committees and in accordance with their data access protocols. Conflict of interest Authors declare no conflicts of interest. Financial support We are grateful to everyone who participated in this research and to everyone who worked on this project and made it possible. This research is part of the TRacking Adolescents’ Individual Lives Survey (TRAILS). Participating centers of TRAILS include the University Medical Center and University of Groningen, the University of Utrecht, and the Parnassia Psychiatric Institute, all in the Netherlands. TRAILS has been financially supported by various grants from the Netherlands Organization for Scientific Research NWO (Medical Research Council program grant GB-MW 940-38-011; ZonMW Brainpower grant 100-001-004; ZonMw Risk Behavior and Dependence grant 60-60600-97-118; ZonMw Culture and Health grant 261-98-710; Social Sciences Council medium-sized investment grants GB-MaGW 480-01-006 and GB-MaGW 480-07-001; Social Sciences Council project grants GB-MaGW 452-04-314 and GB-MaGW 452-06-004; ZonMw Longitudinal Cohort Research on Early Detection and Treatment in Mental Health Care grant 636340002; NWO large-sized investment grant 175.010.2003.005; NWO Longitudinal Survey and Panel Funding 481-08-013 and 481-11-001; NWO Vici 016.130.002, 453-16-007/2735, and Vi.C.191.021; NWO Gravitation 024.001.003), the Dutch Ministry of Justice (WODC), the European Science Foundation (EuroSTRESS project FP-006), the European Research Council (ERC-2017-STG-757364 and ERC-CoG-2015-681466), Biobanking and Biomolecular Resources Research Infrastructure BBMRI-NL (CP 32), the Gratama foundation, the Jan Dekker foundation, the participating universities, and Accare Centre for Child and Adolescent Psychiatry. 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Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochemia medica , 31 (1), 27–53. https://doi.org/https://doi.org/10.11613/BM.2021.010502 Sokol, Y., Levin, C., Linzer, M., Rosensweig, C., Hubner, S., Gromatsky, M., Walsh, S., Dixon, L., & Goodman, M. (2022). Theoretical model of recovery following a suicidal episode (COURAGE): scoping review and narrative synthesis. BJPsych Open , 8 (6), e200, Article e200. https://doi.org/10.1192/bjo.2022.599 Solmi, M., Radua, J., Olivola, M., Croce, E., Soardo, L., Salazar de Pablo, G., Il Shin, J., Kirkbride, J. B., Jones, P., Kim, J. H., Kim, J. Y., Carvalho, A. F., Seeman, M. V., Correll, C. U., & Fusar-Poli, P. (2022). Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry , 27 (1), 281–295. https://doi.org/10.1038/s41380-021-01161-7 Statistics Netherlands. (2026). Young people . https://www-cbs-nl.proxy-ub.rug.nl/en-gb/visualisations/dashboard-population/age/young-people Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ , 338 , b2393. https://doi.org/10.1136/bmj.b2393 Stone, A. A., Schneider, S., & Smyth, J. M. (2023). Evaluation of Pressing Issues in Ecological Momentary Assessment. Annual Review of Clinical Psychology , 19 (Volume 19, 2023), 107–131. https://doi.org/https://doi.org/10.1146/annurev-clinpsy-080921-083128 Teismann, T., Forkmann, T., Glaesmer, H., Egeri, L., & Margraf, J. (2016). Remission of suicidal thoughts: Findings from a longitudinal epidemiological study. Journal of Affective Disorders , 190 , 723–725. https://doi.org/https://doi.org/10.1016/j.jad.2015.09.066 ten Have, M., de Graaf, R., van Dorsselaer, S., Verdurmen, J., van't Land, H., Vollebergh, W., & Beekman, A. (2009). Incidence and Course of Suicidal Ideation and Suicide Attempts in the General Population. The Canadian Journal of Psychiatry , 54 (12), 824–833. https://doi.org/10.1177/070674370905401205 Tops, M., Riese, H., Oldehinkel, A. J., Rijsdijk, F. V., & Ormel, J. (2008). Rejection sensitivity relates to hypocortisolism and depressed mood state in young women. Psychoneuroendocrinology , 33 (5), 551–559. https://doi.org/https://doi.org/10.1016/j.psyneuen.2008.01.011 Tremblay, R. E., Vitaro, F., Gagnon, C., Piché, C., & Royer, N. (1992). A Prosocial Scale for the Preschool Behaviour Questionnaire: Concurrent and Predictive Correlates. International Journal of Behavioral Development , 15 (2), 227–245. https://doi.org/10.1177/016502549201500204 Ungar, M., Ghazinour, M., & Richter, J. (2013). Annual Research Review: What is resilience within the social ecology of human development? Journal of Child Psychology and Psychiatry , 54 (4), 348–366. https://doi.org/https://doi.org/10.1111/jcpp.12025 Van Meter, A. R., Algorta, G. P., Youngstrom, E. A., Lechtman, Y., Youngstrom, J. K., Feeny, N. C., & Findling, R. L. (2018). Assessing for suicidal behavior in youth using the Achenbach System of Empirically Based Assessment. European Child & Adolescent Psychiatry , 27 (2), 159–169. https://doi.org/10.1007/s00787-017-1030-y Van Meter, A. R., Knowles, E. A., & Mintz, E. H. (2023). Systematic Review and Meta-analysis: International Prevalence of Suicidal Ideation and Attempt in Youth. Journal of the American Academy of Child & Adolescent Psychiatry , 62 (9), 973–986. https://doi.org/https://doi.org/10.1016/j.jaac.2022.07.867 van Vuuren, C. L., van der Wal, M. F., Cuijpers, P., & Chinapaw, M. J. M. (2020). Are suicidal thoughts and behaviors a temporary phenomenon in early adolescence? Crisis . https://doi.org/10.1027/0227-5910/a000680 Voss, C., Ollmann, T. M., Miché, M., Venz, J., Hoyer, J., Pieper, L., Höfler, M., & Beesdo-Baum, K. (2019). Prevalence, Onset, and Course of Suicidal Behavior Among Adolescents and Young Adults in Germany. JAMA Network Open , 2 (10), e1914386–e1914386. https://doi.org/10.1001/jamanetworkopen.2019.14386 Weir, K., & Duveen, G. (1981). FURTHER DEVELOPMENT AND VALIDATION OF THE PROSOCIAL BEHAVIOUR QUESTIONNAIRE FOR USE BY TEACHERS. Journal of Child Psychology and Psychiatry , 22 (4), 357–374. https://doi.org/https://doi.org/10.1111/j.1469-7610.1981.tb00561.x White, I. R., Royston, P., & Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine , 30 (4), 377–399. https://doi.org/https://doi.org/10.1002/sim.4067 Whitlock, J., Prussien, K., & Pietrusza, C. (2015). Predictors of self-injury cessation and subsequent psychological growth: results of a probability sample survey of students in eight universities and colleges. Child and Adolescent Psychiatry and Mental Health , 9 (1), 19. https://doi.org/10.1186/s13034-015-0048-5 World Health Organization. (2025). Suicide worldwide in 2021: Global health estimates. https://www.who.int/publications/i/item/9789240110069 Zhu, X., Tian, L., & Huebner, E. S. (2019). Trajectories of Suicidal Ideation from Middle Childhood to Early Adolescence: Risk and Protective Factors. Journal of Youth and Adolescence , 48 (9), 1818–1834. https://doi.org/10.1007/s10964-019-01087-y Footnotes Logistic regressions replaced the planned discriminant analysis, see Table S1 for argumentation. Additional Declarations No competing interests reported. Supplementary Files TRAILSSupplementalmaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor invited by journal 14 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 03 Apr, 2026 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-9314087","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626460357,"identity":"ebd2130d-f2f7-464e-8ae6-dc86c4545ad4","order_by":0,"name":"Mandy Gijzen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBADHsYGIPkBzGZuIF4L4wwwm5E4LRDjeYjRotvAY8B0g6FWhrn/8LHHtnsO2zVIN+LXYnaAx4A5h+E40GHH0o1znh1ObpA5SEgL7waglmM8jI09ZtI5Bw4nM0gkEqulmf+btAUJWmp4GNt42KQZDhy2I6zlMP+HwzkGB3gYe9jMJHsOpCewEdRyvC3xcU5Fnb1h/+FnEj8OWNvzSyQfwKuFgZmB4QCDwWEGQ6jRiW341cNBHYM8lGVPpI5RMApGwSgYQQAAK3xB3YrxHucAAAAASUVORK5CYII=","orcid":"","institution":"Groningen University","correspondingAuthor":true,"prefix":"","firstName":"Mandy","middleName":"","lastName":"Gijzen","suffix":""},{"id":626460358,"identity":"a55e58f5-2267-48f8-8d44-addf68d9e66f","order_by":1,"name":"Bertus Jeronimus","email":"","orcid":"","institution":"Groningen University","correspondingAuthor":false,"prefix":"","firstName":"Bertus","middleName":"","lastName":"Jeronimus","suffix":""},{"id":626460359,"identity":"c68ae465-fd59-474c-91de-1483f7ade710","order_by":2,"name":"Diana Bergen","email":"","orcid":"","institution":"Groningen University","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Bergen","suffix":""},{"id":626460362,"identity":"dfde7bda-0b65-4d2f-906f-d4b89b2d6a98","order_by":3,"name":"Albertine J Oldehinkel","email":"","orcid":"","institution":"University of Groningen, University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Albertine","middleName":"J","lastName":"Oldehinkel","suffix":""}],"badges":[],"createdAt":"2026-04-03 14:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9314087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9314087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107704502,"identity":"4172a215-a8a4-477e-9cf9-44d79e8f3beb","added_by":"auto","created_at":"2026-04-24 08:45:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":764459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9314087/v1/cd615873-3fbe-48af-8f5c-6771022c0b9e.pdf"},{"id":107465270,"identity":"4b84560f-e5ad-42b0-a703-2409df1d3032","added_by":"auto","created_at":"2026-04-21 18:12:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49006,"visible":true,"origin":"","legend":"","description":"","filename":"TRAILSSupplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9314087/v1/bf7ab13f0cb4b10b29c4bb5b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Course and Remission of Suicidality from Ages 11 to 29: Findings from a Dutch Population-Based Longitudinal Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSuicidality is a multidimensional construct that encompasses suicidal thoughts (i.e., suicidal ideation), plans, gestures, self-harm, attempts, and suicide completion, without implying a fixed or linear progression between these elements (Gijzen et al., 2026). Suicide is a leading cause of death among adolescents (ages 12\u0026ndash;18) and emerging adults (ages 18\u0026ndash;30), and on the rise, despite increasing prevention efforts (World Health Organization, 2025). The prevalence of youth suicidal ideation ranges from 14% to 23%, while suicide attempts and self-inflicted injuries occur in approximately 5% to 16% of adolescents (such as self-poisoning or cutting/burning) (Brunner et al., 2014; Carballo et al., 2020; Van Meter et al., 2023). One fifth (~\u0026thinsp;21%) of all European individuals reported a dead wish at some point during their lives (see Castillejos et al., 2021). Suicidality describes \u0026ldquo;the risk of suicide, usually indicated by suicidal ideation or intent, especially as evident in the presence of a well-elaborated suicidal plan\u0026rdquo; (American Psychological Association, 2018). Suicidal behavior includes self-harm, suicide attempts, and suicide completion.\u003c/p\u003e \u003cp\u003eSuicidality typically emerges between ages twelve and thirty but shows great individual variation in how thoughts and behaviors evolve over time (Cipriano et al., 2017; Goldston et al., 2016; Knipe et al., 2022; Naghavi, 2019). Suicidality is often experienced as a transient episode, although a third of young people follow more persistent or severe trajectories (Barrocas et al., 2015; Erausquin et al., 2019), which may require intensive professional care. Suicidality researchers typically focus on the emergence, escalation and persistence of suicidal ideation and self-harm, and risk factors, such as mental health problems, trauma, or impulsivity (Carballo et al., 2020). Far less is known about \u003cem\u003edecreases\u003c/em\u003e in suicidality (ideation or self-harming) over time, and the processes that underlie recovery, which are crucial for positive and strengths-based approaches to suicide prevention and intervention (Gijzen et al., 2026).\u003c/p\u003e \u003cp\u003eProspective studies on the onset and persistence of suicidality indicate that ~\u0026thinsp;70% of youth show declines in suicidal ideation and self-harm over time (i.e. \u0026ldquo;remission\u0026rdquo;), thus suicidality often disappears in 12\u0026ndash;24 months (e.g., Gunnell et al., 2004; Teismann et al., 2016; ten Have et al., 2009, see Borges et al., 2008) for longer follow-ups). Half of the adolescents who have attempted suicide exhibited an adolescent-limited course with reduced risk of adult suicide attempts (Geoffroy et al., 2022). Natural remission of suicidality is thus common, and among youth who reported self-harm thoughts at baseline, only 31% reported them six months later (Russell et al., 2020). To balance the current emphasis on the emergence and persistence of suicidality (e.g., Franklin et al., 2017; prospectively Geoffroy et al., 2021; Kerr et al., 2008; Mars et al., 2019; Moran et al., 2012), we need prospective population-based cohort studies that quantify rates and identify predictors of symptom reduction, sustained remission, and recovery from suicidality over time, as they are lacking (Gijzen et al., 2026).\u003c/p\u003e\n\u003ch3\u003eRemission and recovery and predictive factors\u003c/h3\u003e\n\u003cp\u003eOne community-based UK cohort study identified distinct trajectories of suicidality (i.e., remission, persistence, and late-onset), but the predictors distinguishing these groups, especially for remission, remain poorly understood (Mulholland et al., 2023). Remission from suicidality has been associated with social support, purpose in life, and positive mental health (Herzog et al., 2022; Teismann et al., 2016), but the patterns prove neither clear nor consistent (Rath et al., 2021). An US population study reported that remission from suicidal ideation and attempts was more likely among women and people with higher income, who were married or single (vs. divorced), and those without sleep problems, childhood abuse, or recent psychiatric diagnoses (Fuller-Thomson et al., 2019). Social relationship quality (warmth or confidentiality) and quantity and perceived social support (especially from parents) and family financial status predicted cessation of suicidal ideation and self-injury (vs. persistence) among adolescents and emerging adults (e.g., Rajhvajn Bulat et al., 2024; Whitlock et al., 2015), and better emotion regulation strategies and more life satisfaction.\u003c/p\u003e \u003cp\u003eRecovery from suicidality proved affected by various factors, often interpersonal (e.g., social or practical support), intrapersonal (e.g., psychological wellbeing i.e., coping, self-acceptance, autonomy, personal development), contextual (e.g., demographics [gender, education, partner, child], societal awareness and stigma), and clinical or treatment-related factors (Gijzen et al., 2026; Grimmond et al., 2019; Lakeman \u0026amp; FitzGerald, 2008; Lewin et al., 2024; Sokol et al., 2022). Evidently both micro-level determinants (psychosocial and clinical processes) and meso-level contextual conditions (socioeconomic position, family stability, access to resources) are important in shaping recovery trajectories. A multilevel system perspective in keeping with the exposome framework in which health outcomes such as recovery from suicidality is the outcome of a dynamic and cumulative interplay of biological, chemical, physical, and social exposures embedded within individuals\u0026rsquo; lived environments (Gudi-Mindermann et al., 2023; Ungar et al., 2013).\u003c/p\u003e \u003cp\u003ePopulation-based research that systematically examines factors associated with decreases in suicidality to full remission during the transition from late adolescence into adulthood is scarce (see for a review (Gijzen et al., 2026), especially in the large prospective cohort studies (Gijzen et al., 2026) that underlie many prevention strategies. Individual-level changes over time can help identify specific mechanisms of natural recovery, and such insights that are often missed when focusing solely on clinical or high-risk samples. Studies of trajectory modeling to identify subgroups based on patterns of suicidality do not directly quantify how many individuals experience a reduction in suicidal thoughts or behaviors between assessment waves.\u003c/p\u003e \u003cp\u003eWe examine the course of suicidality and the potential correlates of remission and recovery across adolescents and young adulthood in the Dutch population-based longitudinal TRAcking Individuals Lives Survey (TRAILS), and focus on natural remission rates during the transition from adolescence to adulthood; which we defined as a \u003cem\u003ereduction in self-reported suicidal ideation and/or self-harming behaviors\u003c/em\u003e between consecutive measurement waves (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our remission is a tangible and observable outcome, but we do not imply full psychosocial recovery, which typically includes broader improvements in well-being, social functioning, or resilience (see Gijzen et al., 2026; Grimmond et al., 2019; Lakeman \u0026amp; FitzGerald, 2008; Lewin et al., 2024; Sokol et al., 2022).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe present study\u003c/h2\u003e \u003cp\u003eWe examine (a) the prevalence and changes in suicidal ideation (SI) and self-harm (SH) behavior between ages 11\u0026ndash;29 in the general population and (b) try to identify factors that associated with these improvements in SI/SH or recovery between waves (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Because the Interpersonal Theory of Suicide (Joiner, 2005) theorizes that changes in perceived belongingness and social support play a key role in suicidality, we focus on potential psychosocial and demographic correlates, including self-esteem, self-efficacy, socioeconomic status, and gender.\u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and population\u003c/h2\u003e\n \u003cp\u003eThis study was preregistered at the Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/Y8ETQ\u003c/span\u003e\u003c/span\u003e ) and used data from the TRacking Adolescents\u0026rsquo; Individual Lives Survey (TRAILS), a large-scale prospective cohort study of Dutch adolescents tracked from adolescence to young adulthood (de Winter et al., 2005; Oldehinkel et al., 2015). In 2001, participants (N\u0026thinsp;=\u0026thinsp;2229; mean age\u0026thinsp;=\u0026thinsp;11.1, SD\u0026thinsp;=\u0026thinsp;0.6; 51% female) were recruited via community registers and primary schools in the northern Netherlands (~\u0026thinsp;76% baseline response rate; de Winter et al., 2005). The TRAILS cohort was examined approximately every 2\u0026ndash;3 years and we used data from the first seven waves (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u0026minus;7\u003c/sub\u003e). Retention, age and gender can be found in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Both youth and their parents completed questionnaires. Teachers were also asked to complete a brief questionnaire for each TRAILS child in their class. Comprehensive information about the cohort is provided elsewhere (Oldehinkel et al., 2015; de Winter et al., 2005). Our deviations from our pre-registered paper strategy are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eYouth suicidal ideation (SI) and self-harm (SH): Their correlation (\u003cem\u003er\u003c/em\u003e) and prevalences (%) in the TRAILS population cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"16\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eWave\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\n \u003cp\u003eAge (Mean, SD) and Sex (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\n \u003cp\u003ePrevalence (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\" nameend=\"c16\" namest=\"c11\"\u003e\n \u003cp\u003eChanges (+/-) per wave (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e♀\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003eSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003eSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2000-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eYSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2003-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eYSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2006-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eYSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2009-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eASR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2012-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eASR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2016-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eASR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\" colname=\"c2\"\u003e\n \u003cp\u003e2019-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eASR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003cem\u003eNote.\u003c/em\u003e All correlations were significant at \u003cem\u003ep\u003c/em\u003e\u0026lt;.001 (two-tailed). ♀= proportion women. \u003cstrong\u003eASR\u003c/strong\u003e= Adult self report (126 items). \u003cstrong\u003eM\u003c/strong\u003e= mean. \u003cstrong\u003eN\u003c/strong\u003e= number of participants available at each wave. \u003cstrong\u003eSD\u003c/strong\u003e= Standard Deviation. \u003cstrong\u003er\u003c/strong\u003e= correlation coefficient between SI and SH. \u003cstrong\u003eSH\u003c/strong\u003e= Self-Harm behavior (YSR item 18 and ASR item 15). \u003cstrong\u003eSI\u003c/strong\u003e= Suicidal Ideation (YSR item 91 and ASR item 76). \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e= baseline wave at time point 1. \u003cstrong\u003eYSR\u003c/strong\u003e= Youth Self Report (112 items; Achenbach \u0026amp; Rescola, 2001). We show the proportion of participants who were stable or increased (+) or decreased (-) in symptoms (S\u003csub\u003ex\u003c/sub\u003e) of suicidality between measurement waves (e.g., \u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u0026minus;7\u003c/sub\u003e), and details on these changes are provided in Supplementary Tables S2 (SH) and S3 (SI).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eOur primary outcomes were suicidal ideation (SI) and self-harm (SH) and a priori determined putative recovery factors comprised individual factors, health-related factors, and social factors.\u003c/p\u003e\n\u003ch3\u003eSuicidality\u003c/h3\u003e\n\u003cp\u003eSuicidal ideation (SI) and self-harm (SH) were measured with 2 items per wave between ages 11 and 29 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), each rated on a three-point scale ranging from 0\u0026thinsp;=\u0026thinsp;not at all true to 2\u0026thinsp;=\u0026thinsp;very often or very true. A higher score indicates more SI/SH. The Youth Self Report (YSR) and Adult Self Report (ASR) have been shown to be reliable and valid tools for assessing adolescents aged 11 to 18, based on research involving participants from 23 different countries and various gender and age groups (Ivanova et al., 2007). We measured \u003cem\u003esuicidal ideation\u003c/em\u003e with items 91 of the YSR and item 76 of the ASR and \u003cem\u003eself-harm behavior\u003c/em\u003e with items 18 of the YSR and item 15 of the ASR. Self-harm behavior as measured with the YSR and ASR includes harming oneself (regardless of intention) and attempted suicide.\u003c/p\u003e\n\u003cp\u003eFor both prevalence estimation and logistic regression analyses, the three-point scale was dichotomized: scores of 0 were coded as absence of SI/SH, and scores of 1 or 2 were coded as presence of SI/SH. Consequently, prevalence (%) and regression outcomes reflect the presence versus absence of at least mild suicidal ideation or self-harm.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSociodemographic factors\u003c/h2\u003e\n \u003cp\u003eWe measured \u003cem\u003esex and age\u003c/em\u003e at each wave via self or parent report (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and participants reported about their \u003cem\u003esexual orientation (straight, gay, or bisexual)\u003c/em\u003e during waves \u003cem\u003eT\u003c/em\u003e\u003csub\u003e4\u0026ndash;6\u003c/sub\u003e. We based \u003cem\u003esocioeconomic status (SES)\u003c/em\u003e on parental education, profession, and income. Parents reported on their own and their child\u0026apos;s religiosity, from expression of faith in daily life to perceived centrality of faith in their child\u0026apos;s existence.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSocial wellbeing and social skills\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eSources of wellbeing.\u003c/em\u003e The Social Production Function (SPF; Ormel et al., 1997) instrument was used to capture how adolescents thought they were perceived by their parents, friends, classmates and teachers, which was used to quantify how need satisfaction creates subjective well-being (following SPF theory). The items are rated on a 5-point Likert scale from never to always with higher scores indicating more social well-being (Nieboer et al., 2005). The SPF questionnaire was used to assess the satisfaction of affection, behavioural confirmation, status, stimulation, and comfort needs, from the participants\u0026rsquo; perspective of their parents, teachers, classmates and peers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSchool belongingness.\u003c/em\u003e Three questions on adolescent school belongingness were developed by TRAILS and completed by a teacher (acceptable reliability \u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e: \u0026alpha;\u0026thinsp;=\u0026thinsp;0.55; \u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e: \u0026alpha;\u0026thinsp;=\u0026thinsp;.50).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSocial skills.\u003c/em\u003e The Social Skills Rating System (Gresham \u0026amp; Elliott, 1990) was completed by both teacher and parents. It assesses three subscales: Cooperation, assertion, and self-control. Only assertion was used for the present study. Items are rated as 0\u0026thinsp;=\u0026thinsp;\u003cem\u003enot true\u003c/em\u003e, 1\u0026thinsp;=\u0026thinsp;\u003cem\u003esomewhat or sometimes true\u003c/em\u003e, or 2\u0026thinsp;=\u0026thinsp;\u003cem\u003every often or often true\u003c/em\u003e. One example item read: \u0026lsquo;My child starts conversations by himself instead of waiting for others to initiate communication\u0026rsquo;. Reliability was good at T1: Teacher, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.88; Parent, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.75).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProsocial behavior.\u003c/em\u003e The prosocial behavior questionnaire (Tremblay et al., 1992; Weir \u0026amp; Duveen, 1981) comprised 11 items to assess whether an adolescent has the tendency to react prosocially to various situations (e.g., \u0026ldquo;Asks an outsider to join in during a game?\u0026rdquo;) and was completed by teachers and showed high reliability (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e: \u0026alpha;\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e\n\u003ch3\u003eFuture expectations\u003c/h3\u003e\n\u003cp\u003eParticipants indicated the likelihood of various life events occurring in ten years (e.g., employment status, partnership, parenthood) on a 5-point scale from \u0026apos;Very low\u0026apos; to \u0026apos;Very high\u0026apos;. We only included the items regarding future part-time and full-time job expectations.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eIndividual traits and coping\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eFear of negative social evaluation.\u003c/em\u003e Fear of Negative Social Evaluation was assessed using 5 items based on the scale used by (Tops et al., 2008). The total score is calculated by the sum of all items. Items are scored on a 4-point scale (not at all true, hardly true, moderately true, exactly true), with good reliability (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.79).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSelf-efficacy.\u003c/em\u003e The Generalized Self-Efficacy (GSE: Schwarzer \u0026amp; Jerusalem, 1995) was used to measure self-efficacy. The total score is calculated by finding the sum of all items. Items are scored on a 4-point scale (not at all true, hardly true, moderately true, exactly true). For the total GSE, the total score ranges between ten and 40, with a higher score indicating more self-efficacy. Only a subset of five out of the ten of the full scale were used. The reliability was good (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.80).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCoping.\u003c/em\u003e The Adolescent Cognitive Style Questionnaire (ACSQ; Hankin \u0026amp; Abramson, 2002) was used to measure cognitive coping styles, which consists of ten items. It inquires how the participant would react to two different situations (not having a partner and not doing a good job at work or school). The ACSQ measures five different cognitive styles, namely ASCQ: Situation attributed to internal causes (two items), Situation attributed to stable causes (two items), Situation attributed to global causes (two items), Negative inferences for consequences (two items), Negative inference for self (two items). The reliability of the total scale with ten items was high (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.83)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eFamily environment\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eParental rearing styles.\u003c/em\u003e Parental rearing styles were measured with the Egna Minnen Betraffende Uppfostran (EMBU-C: Markus et al., 2003) which consists of three subscales: overprotection, emotional warmth, and rejection. Overprotection includes fearfulness, anxiety for the child\u0026rsquo;s safety, guilt induction, and intrusiveness: \u0026ldquo;When you come home, you have to tell your parents what you have been doing\u0026rdquo;. Emotional warmth refers to giving special attention, praise, unconditional love, and supportive affection: \u0026ldquo;Your parents show that they love you\u0026rdquo;. Rejection involves hostility, punishment, derogation, and blaming of the child: \u0026ldquo;Your parents criticize you in front of others\u0026rdquo;. The \u003cem\u003eT\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e list contains only 8 relevant items related to emotional warmth and rejection. Reliability was good at \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e (18 items, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.91 (father) \u0026alpha;\u0026thinsp;=\u0026thinsp;0.91 (mother)) and at \u003cem\u003eT\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e (Warmth Father: 4 items, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.88; Warmth Mother: 4 items, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.86; Rejection Father: 4 items, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.70; Rejection Mother: 4 items, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.67).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eParental reactions.\u003c/em\u003e Participants were asked to complete questions regarding angry outbursts, problem solving, and guilt inducing reactions from parents. Only the problem-solving items were used for this study, which measured how parents reacted to disagreements, e.g. \u0026lsquo;Your father/mother really wants to understand why you did what you did\u0026rsquo;. The reliability of these items was acceptable at \u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e (father: \u0026alpha;\u0026thinsp;=\u0026thinsp;.81; mother: \u0026alpha;\u0026thinsp;=\u0026thinsp;.77).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eParental monitoring.\u003c/em\u003e Participants were also asked about how their parents talked to them proactively (mother, father, caregiver), and showed interest in their life (parental solicitation), and how the child shared aspects of their life with the parent proactively (child disclosure). Reliability was at \u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e acceptable (disclosure father: \u0026alpha;\u0026thinsp;=\u0026thinsp;.67; disclosure mother: \u0026alpha;\u0026thinsp;=\u0026thinsp;.72; sollicitation father: \u0026alpha;\u0026thinsp;=\u0026thinsp;.76; sollicitation mother: \u0026alpha;\u0026thinsp;=\u0026thinsp;.72). Example items read: \u0026lsquo;If you\u0026apos;ve been out late at night, do you spontaneously tell your father/mother what you\u0026apos;ve done when you get home?\u0026rsquo; (disclosure), and \u0026lsquo;Does your father/mother start a conversation with you about things that happened during a normal school day?\u0026rsquo; (sollicitation).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eParental knowledge.\u003c/em\u003e Parental knowledge questions regarding friends, time spending and substance use (smoking, alcohol, weed) were developed by TRAILS and one example item reads: \u0026lsquo;How much does your father/mother know about who your friends are?\u0026rsquo;. It consists of 8 items (4 for mother and 4 for father). Reliability was acceptable for both mother (\u0026alpha;\u0026thinsp;=\u0026thinsp;.72) and father (\u0026alpha;\u0026thinsp;=\u0026thinsp;.78) items.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFamily functioning.\u003c/em\u003e Family functioning was measured with the Family Assessment Device (FAD: Miller et al., 1985) completed by parents at waves \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e-\u003csub\u003e6\u003c/sub\u003e and comprised 12 statements regarding how the family functions (e.g., \u0026ldquo;We don\u0026rsquo;t get along well in our family\u0026rdquo;). Each statement can be rated using the following four answer categories; \u0026ldquo;strongly agree\u0026rdquo;, \u0026ldquo;agree\u0026rdquo;, \u0026ldquo;disagree\u0026rdquo;, and \u0026ldquo;strongly disagree\u0026rdquo;. A higher score indicates a more negative family functioning. Reliability was good (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u0026minus;6\u003c/sub\u003e: \u0026alpha;\u0026thinsp;=\u0026thinsp;.85-.88).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eRomantic relationships\u003c/h2\u003e\n \u003cp\u003eParticipants were asked whether they were in a romantic relationship with a partner.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eWork\u003c/h2\u003e\n \u003cp\u003eParticipants were asked whether they had a job. If they indicated they did, they were then asked about \u003cem\u003ejob satisfaction\u003c/em\u003e using one item from the Copenhagen Psychosocial Questionnaire (COPSOQ: Kristensen et al., 2005), and \u003cem\u003ework engagement\u003c/em\u003e using the Utrecht Work Engagement Scale (UWES: Schaufeli et al., 2006). The UWES comprised three items, which had a good reliability (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.86). One sample item reads: \u0026lsquo;At work I am bursting with energy\u0026rsquo;. Each item is rated as \u0026lsquo;Sporadically\u0026rsquo;, \u0026lsquo;Occasionally\u0026rsquo;, \u0026lsquo;Regularly\u0026rsquo;, \u0026lsquo;Often\u0026rsquo;, \u0026lsquo;Very often\u0026rsquo;, \u0026lsquo;Always\u0026rsquo; \u0026lsquo;I don\u0026apos;t know\u0026rsquo;, or \u0026lsquo;Not applicable\u0026rsquo;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eLogistic regression was used to examine which factors predicted a decrease in suicidal ideation or self-harm between each wave.\u003csup\u003e1\u003c/sup\u003e Logistic regression has fewer distributional assumptions and can handle correlated predictors more robustly than discriminant analyses can (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), especially when combined with multiple imputation to handle missing data under the Missing at Random (MAR) assumption (Rubin, 1987; Sterne et al., 2009). The use of multiple imputation allows for more accurate parameter estimation and inference compared to complete case analysis or simple imputation methods (White et al., 2011). Moreover, logistic regression directly models the probability of outcome categories, facilitating clear interpretation of odds ratios for individual predictors across waves (Hosmer Jr et al., 2013). Recent methodological recommendations favor logistic regression in longitudinal studies with complex missingness and interrelated predictors (Sterne et al., 2009; White et al., 2011).\u003c/p\u003e\n \u003cp\u003eMultiple imputation was performed separately for each wave and outcome variable using the fully conditional specification (FCS) method implemented in IBM SPSS Statistics. This method is well-suited to handle different missingness patterns and variable sets across timepoints, and it has been shown to produce unbiased parameter estimates under the missing at random (MAR) assumption (Buuren, 2018; Rubin, 1987). For each wave (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u0026minus;7\u003c/sub\u003e) and outcome (improvement in suicidal ideation [SI] and self-harm [SH]), 20 imputed datasets were generated. Improvement was operationalized using change scores, calculated by subtracting the score at the previous wave from the score at the current wave (e.g., \u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e_SI_change\u0026thinsp;=\u0026thinsp;SI at \u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e \u0026ndash; SI at \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e). To ensure that improvement was meaningful, only participants with a score of 1 or 2 (i.e., elevated SI or SH) at the prior wave were considered. Within this subgroup, improvement was defined as a subsequent decrease in score (i.e., a negative change value). Participants with a score of 0 at the previous wave or missing data at either timepoint were excluded from the analysis. These filtered change scores were recoded into a binary outcome variable, where 1 indicated improvement (decline in SI or SH) and 0 indicated no change (stability). Participants with increased scores (worsening) or missing values were not included in the regression models. These binary improvement indicators served as the dependent variables in the logistic regressions.\u003c/p\u003e\n \u003cp\u003eThe imputation models included all independent variables relevant to the specific wave and outcome, which were selected based on theoretical frameworks and prior empirical evidence. Importantly, predictors included variables measured at previous waves to capture temporal dynamics and potential lagged effects (Sterne et al., 2009). Sex and socio-economic status were included as control variables for each wave.\u003c/p\u003e\n \u003cp\u003eLogistic regression analyses were subsequently performed on each imputed dataset to examine predictors of improvement in SI and SH at each wave. Estimates from the 20 imputed datasets were combined using Rubin\u0026rsquo;s rules to obtain pooled odds ratios (ORs) and 95% confidence intervals (CIs; (Rubin, 1987). This wave-specific approach allowed for tailored modeling of predictors at discrete timepoints while appropriately handling missing data without bias introduced by irrelevant variables. Statistical significance was evaluated at p \u0026lt; .05. All analyses were conducted using IBM SPSS Statistics version 28.\u003c/p\u003e\n \u003cp\u003eTo account for the hierarchical structure and conceptual clustering of predictors, blockwise logistic regression analyses were performed (see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For each wave (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u0026minus;7\u003c/sub\u003e) and outcome (SI and SH improvement), predictors were grouped into theoretically coherent blocks, with control variables (sex and SES) entered in the first block (A), and subsequent blocks (B-F) based on developmental relevance and proximity to the outcome (e.g., mental health and wellbeing, school context, work support), which allowed us to evaluate the incremental predictive value of each domain above and beyond more distal factors.\u003c/p\u003e\n \u003cp\u003eThis blockwise approach was chosen to facilitate interpretability, limit multicollinearity, and explore whether more proximal or developmentally salient factors contributed uniquely to improvement in SI/SH. The block structure varied across waves in accordance with the availability of measures and developmental timing. A full overview of block composition per wave is provided in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, an approach that enabled us to examine changes in psychosocial predictors of decreases over time, while accounting for both stability and developmental shifts in relevant domains.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverview of Thematic Predictor Blocks per Wave (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026ndash;\u003cem\u003eT\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\n \u003cp\u003eWave \u0026amp; Block\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eThematic Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eMental health \u0026amp; wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eWarmth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eGroup belongingness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAssertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eSource of wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eMother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFather,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFriends and classmates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFamily context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eParental knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eMother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFather\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSchool context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSchool wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eProsocial beh.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSource of wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eclassmates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eChild wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eChild is doing better than 2 yrs ago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eParental monitoring \u0026amp; reactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eChild disclosure, father\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eChild disclosure, mother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ePaternal sollicitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eMaternal sollicitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFamily context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSchool context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSchool wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSource of wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eClassmates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFamily context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eEMBU-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRomantic relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eYes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eCognitive style \u0026amp; self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eACSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFNSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eGSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eJob expectation in 10 yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFulltime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eParttime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eReligiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eReligosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRomantic relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eYes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFamily functioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRomantic relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eYes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWork support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCOPSOQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUWES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFamily functioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e. Baseline (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e). * For T5 SH there were too few people (n\u0026thinsp;=\u0026thinsp;1) classified as stable as such logistic regression was not possible. \u003cstrong\u003eACSQ\u003c/strong\u003e= Adolescent Cognitive Style Questionnaire. \u003cstrong\u003eCOPSOQ\u003c/strong\u003e= Copenhagen Psychosocial Questionnaire. \u003cstrong\u003eEMBU-C\u003c/strong\u003e= Egna Minnen Betr\u0026auml;ffande Uppfostran Questionnaire for Children. \u003cstrong\u003eFAD\u003c/strong\u003e= Family Assessment Device. \u003cstrong\u003eFNSE\u003c/strong\u003e= Fear of Negative Social Evaluation. \u003cstrong\u003eGSE\u003c/strong\u003e= General Self-efficacy Scale. \u003cstrong\u003eP\u003c/strong\u003e= Parent. \u003cstrong\u003eR\u003c/strong\u003e= report. \u003cstrong\u003eS\u003c/strong\u003e= self-reported. \u003cstrong\u003eSES\u003c/strong\u003e= Socioeconomic Status. Sex (1\u0026thinsp;=\u0026thinsp;male). \u003cstrong\u003eYrs\u003c/strong\u003e= years. \u003cstrong\u003eUWES\u003c/strong\u003e= Utrecht Work Engagement Scale.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe prevalence rates, mean scores, and correlations for suicidal ideation (SI) and self-harm (SH) among the 2229 participants between ages 11 and 29 across the seven waves of the TRAILS cohort (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u0026minus;7\u003c/sub\u003e) are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. SI and SH were measured with single items (YSR ages 11\u0026ndash;16, ASR ages 19\u0026ndash;29) and the prevalence (%) reflects the proportion of participants endorsing at least mild SI or SH, based on dichotomized scores (0\u0026thinsp;=\u0026thinsp;absence; 1 or 2\u0026thinsp;=\u0026thinsp;presence on the original three-point scale). Across adolescence and into young adulthood, SI prevalence ranged from 4.4% to 8.3%, with a peak around age 11 and gradual decline over adolescence and emerging adulthood (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and a modest increase observed at age 26 (6.1%). SH showed a similar, though overall lower, pattern: prevalence ranged from 1.9% to 5.3%, peaking around age 14, and declining more steeply thereafter. This suggests that both SI and SH are most common in early to mid-adolescence, with decreasing frequency as participants transition into adulthood. Despite these age-related declines, the correlation between SI and SH remained consistently moderate and significant across all waves (\u003cem\u003er\u003c/em\u003e= .33 to .53, \u003cem\u003ep\u003c/em\u003e\u0026lt; .001), indicating that youth who reported suicidal ideation were also more likely to report self-harming behavior, regardless of age.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSuicidality over time\u003c/h2\u003e \u003cp\u003eIn the original non-imputed dataset, a small proportion of young people experienced suicidality at each time point (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); suicidal ideation (4.4\u0026ndash;8.3%) was more common than self-harming behaviors (1.9\u0026ndash;5.3%), and both were reported more during early adolescence than late adolescence. We note that most adolescents reported surprisingly stable levels of both suicidal ideation (88.5\u0026ndash;93.5%) and self-harm (92.2\u0026ndash;96.6%) over time, and only a small proportion of participants reported a decrease in SI or SH over time (ranging from 0.1% to 5.6%), fairly comparable to the small proportion who reported an increase (0.1% to 5.1%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFactors related to decreases in suicidality\u003c/h2\u003e \u003cp\u003eTo examine predictors of improvement in SI and SH, logistic regression analyses were conducted for each time point. Since model fit statistics such as the omnibus test of model coefficients, Nagelkerke R\u0026sup2;, and classification accuracy are not pooled across imputed datasets in SPSS, these values were calculated from the logistic regression models based on the original (non-imputed) dataset and should be interpreted as indicative only. Supplementary Table S4 presents the number of cases included and missing data per wave for the original datasets.\u003c/p\u003e \u003cp\u003eAt age 13.6 (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) the final pooled model for suicidal ideation including all predictor blocks (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) did not identify predictors of improvement of both SI and SH. This lack of results could reflect that only eight participants were in the stable-high group. At all other waves, for both the suicidal ideation and self-harm model, we also did not find any significant predictors for improvement. We think the absence of results reflects that too few people were classified as stable-high in suicidality (i.e., persistent suicidality) across two waves for our models, although this was good news for the participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eWe studied the developmental trajectories of suicidal ideation (SI) and self-harm (SH) over adolescence and emerging adulthood and focused on reductions in suicidality, to examine which factors associated with improvement, such as perceived group belongingness and social sources of wellbeing, to examine the Interpersonal Theory of Suicide (Joiner, 2005). Our TRAILS sample of 2,229 adolescents age 11 from the general Dutch population was followed to age 29, which allowed us to examine their suicidality over 7 measurement waves (YSR/ASR) and the rates of natural recovery (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and (b) factors that associate with recovery from SI/SH (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our results show that SI/SH were rare among children, adolescents, and emerging adults, as fortunately\u0026thinsp;~\u0026thinsp;92% did not experience suicidality, and a small percentage of youth experiences either an increase or decrease in suicidality over time (0.1% to 5.6%). The low prevalence of SI/SH and the few participants who improved or recovered in SI/SH precluded robust associations with external factors (low base-rate; (Serdar et al., 2021), thus even with our large population panel of 2,229 adolescents, we proved to be underpowered.\u003c/p\u003e \u003cp\u003eThe prevalence of SI/SH in our sample (measured 2001\u0026ndash;2018) was largely in line with Dutch reports over 2021 (~\u0026thinsp;92.4%, past 3 months; (National Institute for Public Health and the Environment [Rijksinstituut voor de Volksgezondheid en Milieu], 2025), although more recently a small rise has been observed during the COVID-19 pandemic (2022-24, to ~\u0026thinsp;13\u0026ndash;17% of Dutch adolescents). The international SI prevalence of ~\u0026thinsp;16.4% (Van Meter et al., 2023) and for SH of 16.9% (Gillies et al., 2018) indicates that the prevalence in TRAILS was fortunately slightly lower. Youth SI/SH reports were relatively stable across waves (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which appears to be inconsistent with ecological momentary assessment (EMA) and intensive longitudinal studies that demonstrated that SI and associated risk states fluctuate markedly within individuals over hours or days, including sudden shifts from low-to-high-risk states without intermediate planning (Czyz et al., 2019; Czyz et al., 2022; Hallensleben et al., 2019; Kleiman et al., 2017).\u003c/p\u003e \u003cp\u003eTRAILS used two self-report items from the Achenbach System of Empirically Based Assessment (ASEBA) to evaluate suicidality (item #18 \u0026ldquo;deliberately harms self or attempts suicide\u0026rdquo; and #91 \u0026ldquo;talks about killing self\u0026rdquo;), among other emotional, behavioral, and social problems, and these items were able to distinguish youth with a history of suicidal behavior from peers (Van Meter et al., 2018). Such single-item, interval-based self-report measures were designed to study prevalence/incidence in representative population samples with long follow-up, to stratify scores by sociodemographic factors and other between-person factors, but may insufficiently capture short-term variability and transition dynamics, and have limited utility for prospective risk prediction or for detecting nuanced reductions in suicidality across time. Such daily dynamics may also prove unimportant for long-term outcomes, however, and future studies could shed more light on this paradox.\u003c/p\u003e \u003cp\u003eThe three-month window used in ASR/YSR items likely captures low-base-rate outcomes better than short EMA windows because of its longer reference period, but it may also be more vulnerable to recall and reporting biases due to the extended retrospective window and temporal averaging (e.g., Stone et al., 2023). Note that suicide risk scales have demonstrated limited clinical utility for predicting future suicide attempts (Franklin et al., 2017; Runeson et al., 2017), and national clinical guidelines caution against using such tools to predict suicide risk or determine treatment decisions (NICE, 2022).\u003c/p\u003e \u003cp\u003eCoarse measurement can underestimate relationships with proximal predictors, and panel-wave timing may miss the relevant risk window. EMA studies allow researchers to study within-person fluctuations in suicidality and process-oriented models, but are not a golden solution, as they make only sense in high-risk samples, while in general population samples (like ours) the low prevalence of SI/SH (low base rate) and reduced spread also limits statistical power to detect associations with external factors, as has frequently been noted in EMA studies of suicidal thoughts and behaviors due to their low base rate (e.g., Bernanke et al., 2017; Kleiman \u0026amp; Nock, 2018). Additionally, a recent EMA study with suicidal patients in the Netherlands found a completion rate of only 17.6% (Nuij et al., 2022), and EMA assessments are particularly missed during moments of stress (Myroniuk et al., 2026). Recently researchers proposed there may be rapid-fluctuating stress-responsive and more persistent non-stress-responsive suicidal phenotypes, with their own risk factors and pathophysiology (Bernanke et al., 2017), which could suggest different methodologies tap into different types of suicidality.\u003c/p\u003e \u003cp\u003eOur results showed that most youth reported suicidality between ages 11\u0026ndash;16 (TRAILS \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e-\u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e), in line with research that indicates that suicidal ideation generally rises between ages 11 and 17 among US youth (Nock et al., 2013). A German study indicated that suicidal behavior begins to rise around age 10 (\u0026lt;\u0026thinsp;1%), increases gradually to 2.2% by age 12, and then escalates sharply, reaching 13.5% until the age of 20 (Voss et al., 2019). A Dutch study found that suicidal ideation did not persist for most youth between ages 13 and 16 years (van Vuuren et al., 2020). Importantly, low individual-level persistence does not necessarily imply declining prevalence, as increases may occur if new cases emerge during the same period. Furthermore, the global onset of the first mental disorder is before age 14 in one-third of individuals, and half before age 18 (Solmi et al., 2022), in keeping with the aforementioned rise in suicidality over adolescence.\u003c/p\u003e \u003cp\u003eTrajectories of suicidality research has mostly focused on risk factors, but positive self-perceptions and self-esteem (Bakken et al., 2025; Zhu et al., 2019), life satisfaction (Zhu et al., 2019), academic achievement and school connectedness/wellbeing (Bakken et al., 2025; Zhu et al., 2019), family and friend support (Adrian et al., 2016), social adjustment (Goldston et al., 2016) could be identified as potential factors related to decreases in suicidality over time during adolescence. Our analyses could not identify any protective factors or combination of factors that could accurately predict decreases in suicidality over time within the TRAILS cohort, which highlights the complexity of identifying such protective or recovery factors (Gijzen et al., 2026).\u003c/p\u003e \u003cp\u003eIt may not only be difficult to pinpoint protective factors, but also to develop or sustain them over time, particularly in high-risk groups. Confounding by baseline severity of suicidality or related mental health problems can affect our results, e.g., people with high self-esteem tend to report less SI/SH (Buecker et al., 2025), which leaves them less room for improvement over time. Changes in suicidality may partly reflect regression to the mean rather than true improvement (Barnett et al., 2004), and apparently demand more suitable study design and statistical methods. Our study may have also been limited by instruments that were better suited to detect risk rather than protective factors, especially subtle or indirect effects. We demonstrate that the low base-rate of SI/SH in our prospective general population samples of \u0026gt;\u0026thinsp;2.200 adolescents like TRAILS poses a significant barrier to investigate the specific mechanisms of natural recovery in suicidality. Our conclusion that the low base rate of suicidal events necessitates massive population samples echoes a warning by the American National Research Council (American National Research Council, 2002), in hindsight (20/20).\u003c/p\u003e \u003cp\u003eStable correlation estimates with recovery factors require\u0026thinsp;\u0026gt;\u0026thinsp;250 adolescents who recover (improve) in SI/SH (see Sch\u0026ouml;nbrodt \u0026amp; Perugini, 2013), which, at our prevalence rates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, via ~\u0026thinsp;1.5-5% in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and 10\u0026ndash;20% drop-out), means about twenty thousand adolescents. Which would require TRAILS to be nine times larger, and to include three times the number of adolescents aged 11 that were available in the Northern three provinces of the Netherlands, and ~\u0026thinsp;15% of \u003cem\u003eall\u003c/em\u003e adolescents aged 12\u0026ndash;18 that lived there (Statistics Netherlands, 2026). Hence, suicidality research in general may focus more on \u003cem\u003ehow\u003c/em\u003e specific relational \u003cem\u003eprocesses\u003c/em\u003e can alleviate physiological stress responses and buffer youth against the development of suicidality, such as conflict resolution and supportive parenting strategies, as such family interventions can reduce risk of suicidality long-term (e.g., Connell et al., 2023) over a decade), and proves effective across various populations of youth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOne key strength of this study is the high data quality of the TRAILS dataset, which ensures reliable and valid assessments of key variables. The large sample size and rigorous data collection methods enhance the generalizability and robustness of the findings. However, several limitations must be acknowledged. First, as discussed, suicidality is relatively rare, which has limited our statistical power and the ability to detect meaningful effects. Additionally, suicidal ideation and self-harm were measured using a single-item assessment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which, while common in research, may not fully capture the complexity of these constructs, and their potential to fluctuate rapidly within-people over time. Our lack of results at the group-level also stimulates us to study individual-level dynamics, processes, and predictors, as factors may not be protective for all youth. Furthermore, cohort studies like TRAILS tend to emphasize risk factors associated with adverse outcomes, as identifying predictors of psychopathology and suicidality is critical for early intervention. The measurement of protective factors is often comparatively poor, however, which may play a crucial role in resilience and recovery research. A more balanced approach incorporating both risk and protective factors could enhance the predictive accuracy of suicide research and inform more effective prevention strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations for future research\u003c/h2\u003e \u003cp\u003eA recovery-focused approach in a very large longitudinal cohort study could involve not only tracking established protective factors but also incorporating measures of recovery dynamics that go beyond mere symptom reduction. These could include variables that assess post-traumatic growth (e.g., perceived personal growth, meaning-making), shifts in identity (e.g., self-concept, self-worth), or the development of positive future outlooks (e.g., hope for the future, life satisfaction). Moreover, the study could focus on resilience in the face of adversity by measuring adaptability and the ability to manage setbacks, as well as access to adaptive coping mechanisms and how these evolve over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIt remains difficult to find which factors are related to a decrease in either suicidal ideation or self-harm. Few participants experienced a decrease in suicidality, which makes statistical analyses difficult to perform. EMA or qualitative approaches may be better suited for looking at rare and highly fluctuating phenomena such as suicidality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TRAILS protocol was approved by the Central Committee on Research Involving Human subjects (CCMO): NL67411.042.18. Informed consent was obtained of all participating youth and their parents after the nature of the study had been fully explained.\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\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the TRAILS-consortium (www.trails.nl), but restrictions apply to the availability of these data, which were used with specific permission for the current study and are not publicly available. Due to privacy restrictions and ethical regulations, the raw data are not publicly available. Access to the data can be requested through the respective study secretariats, subject to approval by their scientific committees and in accordance with their data access protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to everyone who participated in this research and to everyone who worked on this project and made it possible. This research is part of the TRacking Adolescents\u0026rsquo; Individual Lives Survey (TRAILS). Participating centers of TRAILS include the University Medical Center and University of Groningen, the University of Utrecht, and the Parnassia Psychiatric Institute, all in the Netherlands. TRAILS has been financially supported by various grants from the Netherlands Organization for Scientific Research NWO (Medical Research Council program grant GB-MW 940-38-011; ZonMW Brainpower grant 100-001-004; ZonMw Risk Behavior and Dependence grant 60-60600-97-118; ZonMw Culture and Health grant 261-98-710; Social Sciences Council medium-sized investment grants GB-MaGW 480-01-006 and GB-MaGW 480-07-001; Social Sciences Council project grants GB-MaGW 452-04-314 and GB-MaGW 452-06-004; ZonMw Longitudinal Cohort Research on Early Detection and Treatment in Mental Health Care grant 636340002; NWO large-sized investment grant 175.010.2003.005; NWO Longitudinal Survey and Panel Funding 481-08-013 and 481-11-001; NWO Vici 016.130.002, 453-16-007/2735, and Vi.C.191.021; NWO Gravitation 024.001.003), the Dutch Ministry of Justice (WODC), the European Science Foundation (EuroSTRESS project FP-006), the European Research Council (ERC-2017-STG-757364 and ERC-CoG-2015-681466), Biobanking and Biomolecular Resources Research Infrastructure BBMRI-NL (CP 32), the Gratama foundation, the Jan Dekker foundation, the participating universities, and Accare Centre for Child and Adolescent Psychiatry.\u003c/p\u003e\n\u003cp\u003eBFJ was also financially supported by the Dutch Research Council and the Dutch Ministry of Education, Culture and Science (NWO gravitation grant number 024.005.010 for http://www.stress-in-action.nl).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdrian, M., Miller, A. 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Incidence and Course of Suicidal Ideation and Suicide Attempts in the General Population. \u003cem\u003eThe Canadian Journal of Psychiatry\u003c/em\u003e,\u003cem\u003e\u0026nbsp;54\u003c/em\u003e(12), 824\u0026ndash;833. https://doi.org/10.1177/070674370905401205\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTops, M., Riese, H., Oldehinkel, A. J., Rijsdijk, F. V., \u0026amp; Ormel, J. (2008). Rejection sensitivity relates to hypocortisolism and depressed mood state in young women. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;33\u003c/em\u003e(5), 551\u0026ndash;559. https://doi.org/https://doi.org/10.1016/j.psyneuen.2008.01.011\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTremblay, R. E., Vitaro, F., Gagnon, C., Pich\u0026eacute;, C., \u0026amp; Royer, N. (1992). 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FURTHER DEVELOPMENT AND VALIDATION OF THE PROSOCIAL BEHAVIOUR QUESTIONNAIRE FOR USE BY TEACHERS. \u003cem\u003eJournal of Child Psychology and Psychiatry\u003c/em\u003e,\u003cem\u003e\u0026nbsp;22\u003c/em\u003e(4), 357\u0026ndash;374. https://doi.org/https://doi.org/10.1111/j.1469-7610.1981.tb00561.x\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWhite, I. R., Royston, P., \u0026amp; Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. \u003cem\u003eStatistics in Medicine\u003c/em\u003e,\u003cem\u003e\u0026nbsp;30\u003c/em\u003e(4), 377\u0026ndash;399. https://doi.org/https://doi.org/10.1002/sim.4067\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWhitlock, J., Prussien, K., \u0026amp; Pietrusza, C. (2015). Predictors of self-injury cessation and subsequent psychological growth: results of a probability sample survey of students in eight universities and colleges. \u003cem\u003eChild and Adolescent Psychiatry and Mental Health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;9\u003c/em\u003e(1), 19. https://doi.org/10.1186/s13034-015-0048-5\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. (2025). \u003cem\u003eSuicide worldwide in 2021: Global health estimates.\u003c/em\u003e https://www.who.int/publications/i/item/9789240110069\u003c/li\u003e\n \u003cli\u003eZhu, X., Tian, L., \u0026amp; Huebner, E. S. (2019). Trajectories of Suicidal Ideation from Middle Childhood to Early Adolescence: Risk and Protective Factors. \u003cem\u003eJournal of Youth and Adolescence\u003c/em\u003e,\u003cem\u003e\u0026nbsp;48\u003c/em\u003e(9), 1818\u0026ndash;1834. https://doi.org/10.1007/s10964-019-01087-y \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Logistic regressions replaced the planned discriminant analysis, see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for argumentation.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Suicidality, Remission, Protective factors, Epidemiology, Longitudinal cohort","lastPublishedDoi":"10.21203/rs.3.rs-9314087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9314087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSuicide is a leading cause of death among adolescents and emerging adults. Suicidality is a multidimensional construct that includes suicidal ideation (SI), self-harm (SH), suicide attempts, and suicide completion, without a fixed or linear progression between these elements. We examined the course of suicidality (SI/SH) over adolescence and young adulthood (age 11\u0026ndash;29) and correlates of natural remission and recovery over these 18 years, using the Dutch population-based longitudinal TRAcking Individuals Lives Survey (TRAILS). We examined whether perceived belongingness and social support are associated with recovery from suicidality, for example, as predicted by the Interpersonal Theory of Suicide.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTRAILS followed 2229 adolescents aged 11 (SD\u0026thinsp;=\u0026thinsp;0.6, 49% women) and we studied to age 29, with 7 waves 2\u0026ndash;3 years apart. The prevalence of suicidality and changes therein were quantified using the Adolescent/Youth Self-report (Achenbach system), and we examined potential psychosocial and demographic correlates, such as self-esteem, self-efficacy, temperament/personality, and cognitive style, socioeconomic status, and gender.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe correlation between SI/SH increased from early adolescence (age 11 \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;~\u0026thinsp;.33) to stabilize over mid adolescence (ages 15\u0026ndash;29, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;~\u0026thinsp;.50). SI was somewhat more common in youth (4.4\u0026ndash;8.3%) than SH was (1.9%-5.3%) and peaked earlier in time (SI, age 11) than SH (age 14), and suicidality declined gradually over adolescence and young adulthood. The low base-rate of SI/SH in our prospective general population sample proved a significant barrier to investigate the specific mechanisms of natural recovery in suicidality, as luckily\u0026thinsp;\u0026gt;\u0026thinsp;90% of youth reported no suicidality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eProtective and recovery factors for SI and SH remain difficult to identify, and apparently require general population studies of twenty thousand adolescents or more in the Western world. Complementary risk samples and time series or process approaches, interviews, and psychological autopsy studies can help identify prevention and protective factors to reduce suicidality among youth.\u003c/p\u003e","manuscriptTitle":"The Course and Remission of Suicidality from Ages 11 to 29: Findings from a Dutch Population-Based Longitudinal Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 18:12:43","doi":"10.21203/rs.3.rs-9314087/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T13:39:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192671455828718357093179034213319637421","date":"2026-04-15T14:40:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T11:17:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-14T07:47:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T14:04:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T14:04:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-04-03T14:40:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7819b32c-4d01-4658-b9c2-3284984c5b09","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T13:39:11+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T18:12:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 18:12:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9314087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9314087","identity":"rs-9314087","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}