Abstract
Objective
Response to trauma varies from post-traumatic distress (PTS) to post-traumatic growth (PTG) seemingly depending on internal and external individual resources. This study examines the relationships among trait Emotional Intelligence (trait EI), sleep quality, adaptive coping, and differential outcomes of trauma, namely PTS and PTG, in adolescents exposed to collective chronic adversities, including the health pandemic and sociopolitical instability.
Methods
This cross-sectional study involved 612 high school students (323 women) aged between 14 and 21 (M = 16.44, SD = 1.25) from Lebanese public schools. Measures assessed trait EI through the (TEI-QUE), adaptive coping (Brief-COPE), post-traumatic stress (PCL-5), sleep quality through the Sleep Quality Assessment, and post-traumatic growth (PTGI-SF). Structural equation modelling (SEM) was performed with robust estimators.
Results
Trait EI directly and indirectly associated with PTS and PTG. We found positive relationships between trait EI and adaptive coping (β = 0.58, p < .001) and trait EI and sleep quality (β = 0.37, p < .001), and between adaptive coping, and both PTS (β = 0.46, p < .001) and PTG (β = 0.43, p < .001). In addition, we found negative relationships between sleep and PTS (β = − 0.1, p < .05) and PTG (β = − 0.15, p < .001). The pathways involving adaptive coping and sleep quality were significantly associated with both PTS (β = 0.23, p < .001) and PTG (β = 0.19, p < .001), respectively, in relation to trait EI.
Conclusions
Findings indicate that trauma outcomes are associated with trait EI, adaptive coping strategies, and sleep quality. The study advocates for the integration of these evidence-based cognitive and behavioral trainings in educational programs designed for adolescents in high-risk environments.
Keywords: Trauma, Distress, Growth, Adolescents, Sleep, Emotional intelligence
Introduction
Adolescence is a developmental stage characterized by rapid biological, psychological transitions that increase vulnerability to stress and trauma [1]. This vulnerability is particularly acute for the Lebanese adolescent sample in this study, who have navigated a series of massive overlapping crises [2]. These include a prolonged socio-economic collapse, the political instability that has been persistent for several years, the catastrophic 2020 Beirut port explosion, with a backdrop of a global COVID-19 pandemic [3, 4]. Such collective chronic adversities have led to high reported rates of depression, anxiety and Post-Traumatic Stress Disorder (PTSD) alongside pervasive sleep disturbances [2, 5]. While exposure to these stressors often leads to persistent distress, some individuals demonstrate post-traumatic growth (PTG)- a positive psychological transformation following adversity [6]. Notably, some experience both distress and growth, as PTSD and PTG may co-occur or unfold successively rather than manifesting as mutually exclusive trajectories [7].
Understanding why adolescents’ responses vary so significantly despite similar exposure remains a critical challenge in trauma research. Extant research has largely focused on risk factors, such as demographic, environmental, or contextual stressors, to explain variations in distress [8]. While these approaches clarify vulnerability pathways, they provide limited insight into the intrapersonal resources that may enable adolescents to cope adaptively and protect against adverse effects and promote growth. To address this, we investigated trait Emotional Intelligence (trait EI) as a foundational intrapersonal resource.
Trait emotional intelligence as a foundational resource
Trait Emotional Intelligence (Trait EI) consists of self-perceived emotional abilities, encompassing capacities to perceive, understand, manage, and express emotions in both personal and social contexts [9]. Trait EI captures how individuals evaluate their emotional skills and their confidence in navigating emotionally demanding situations. In adolescents, higher Trait EI is linked to enhanced socioemotional competence, lower rates of internalizing difficulties, lower rates of anxiety and depression, greater overall psychological well-being and lower psychopathology [10–12]. Conversely, lower trait EI has been linked to maladaptive behaviors in adolescents, such as aggression and bullying, and to elevated risk of internalizing difficulties [13, 14], and especially to adaptive ones such as planning, active coping, or positive reframing (e.g., [15, 16].
The cognitive-behavioral pathway: adaptive coping
Adaptive coping involves the cognitive and behavioral efforts—such as problem-solving, cognitive reframing, and seeking social support—required to manage stressful experiences [17]. Adolescents with high trait EI are predisposed to employ these strategies, which assist in reinterpreting traumatic events as manageable rather than purely threatening [11, 17, 18]. Conversely, low trait EI is linked to maladaptive patterns such as avoidance and disengagement, which exacerbate distress and impede recovery [19]. Because adaptive coping fosters meaning-making and social connection, it acts as a critical mechanism through which trait EI reduces Post-Traumatic Stress (PTS) and promotes Post-Traumatic Growth (PTG) [20–22].
The biological pathway: sleep quality
Parallel to cognitive efforts, trait EI influences trauma outcomes through the biological restorative process of sleep. Sleep is essential for emotional regulation and neurocognitive functioning [23]. In adolescence, high trait EI promotes superior sleep quality by reducing cognitive-emotional arousal and improving stress management [23, 24]. This is particularly vital in trauma contexts, where restorative sleep may buffer against intrusive symptoms and facilitate the integration of traumatic memories into a growth-oriented narrative [25].
Synthesis: shared mechanisms for PTS and PTG
Emerging evidence suggests that PTS and PTG are coexisting constructs that share underlying mechanisms, such as deliberate rumination and emotional processing [26, 27]. Within this framework, trait EI emerges as a "shared mechanism" that governs the transition from intrusive distress to meaningful growth [28]. By mobilizing both behavioral resources (adaptive coping) and biological resilience (sleep quality), trait EI provides the necessary framework for adolescents to reduce PTS severity while simultaneously fostering PTG [29, 30]. This integrated pathway highlights the role of sleep and coping as the functional links between internal emotional traits and long-term psychological recovery [31, 32].
The present study
In such a stress-laden context as Lebanon, intrapersonal resources such as trait EI become critical, as they are associated with meaning-focused coping [30], more stable sleep quality despite ongoing instability [33], and both trajectories of trauma outcomes including lower PTS symptoms and higher PTG [34]. Our study addresses the existing gap as previous research largely focused on contextual stressors (risk factors) rather than intrapersonal resources (trait EI) that enable resilience in high-chronic-stress environments. Understanding the mechanisms through which trait EI supports adolescents’ adjustment following traumatic exposure is therefore essential to build targeted intervention and prevention policies.
In the present study, we use a structural model to examine whether adaptive coping and perceived sleep quality are part of the relationship between trait EI and both trajectories of trauma outcomes, PTS and PTG, in Lebanese high school adolescents. The novelty relates to the contextual aspect within an underrepresented age-group in the Middle East, facing a unique "compounded weight" of adversities. We examine an integrated path examining EI, coping, and sleep not as independent variables but rather as an integrative mediational chain for both distress and growth outcomes in Lebanese youth.
We propose the following hypotheses:
H1
Trait EI, Adaptive Coping and Sleep positively associated with PTGI and negatively associated with PTS.
H1a
Trait EI will directly positively relate to PTG and negatively relate to PTS.
H1b
Trait EI will positively relate to the mediators (Adaptive Coping and Sleep Quality).
H1c
Adaptive Coping and Sleep Quality will significantly relate to trauma outcomes (PTS and PTG), serving as mediating pathways.
H2
Trait EI is positively associated with Adaptive Coping and Sleep Quality.
H3
Trait EI has an indirect association with PCL-5 and PTGI through Adaptive Coping.
H4
Trait EI has an indirect association with PCL-5 and PTGI through Sleep Quality.
Methods
Procedure
The study was part of a larger project Yes to Emotions in Youth (YEY): An Emotional Intelligence Training for Vulnerable Youth in Lebanon, funded by Grand Challenges Canada – Global Mental Health (NIHS; R-GMH-POC-2107-47498). This study received administrative approval from the Lebanese Ministry of Education and Higher Education (MEHE) and was approved by the Institutional Review Board (LAU IRB;reference number: LAU.SAS.MJ1.28/Oct/2021). After participants and their parents signed the informed consent, participants were asked to report some demographic data and then filled out the validated Arabic versions of standardized scales. All Arabic scales were reviewed to ensure item clarity and cultural appropriateness.
Participants
This cross-sectional study involved a sample of 612 high school students (323 women, 270 men, and 19 identifying as other gender) aged between 14 and 21 years (M = 16.44, SD = 1.25), recruited from three public schools in Lebanon. Schools were selected from a list provided by the Ministry of Education and Higher Education (MEHE) and represented different geographic regions of the country. All participating schools followed the national public-school curriculum, ensuring comparable educational contexts, and single-sex schools were excluded.
Inclusion criteria comprised students aged 14 years or older enrolled in public high schools. Participants who disclosed acute suicidal ideation or significant suicide risk during the assessment process were excluded and referred to appropriate psychological support by trained youth leaders with psychology backgrounds, in accordance with informed consent procedures. In addition to age, gender, and class, demographic information included parents’ employment status and perceived difficulty paying household bills.
Measures
Trait Emotional Intelligence Questionnaire for Adolescents- Short Form (TEIQue-ASF [35]), designed to assess overall trait EI. Items are rated on a 7-point Likert scale ranging from 1 (“Completely disagree”) to 7 (“Completely agree”). The TEIQue has been adapted into more than 20 languages [18] and has demonstrated strong psychometric properties across diverse samples. In addition, the measure has been validated in Arabic-speaking samples [36] and the English version has shown good psychometric properties in Lebanon specifically [37]. In the present sample, internal consistency was satisfactory (α = 0.79; ⍵ = 0.83).
Brief coping orientation to problems experienced inventory (Brief-COPE, [38], is a self-report tool that evaluates adaptive coping, maladaptive coping, and support-seeking strategies. For this study, analysis was limited to eight items representing four adaptive subscales (i.e., active coping, planning, acceptance, and positive reframing), as they were considered most relevant to the study’s focus and consistent with the reviewed literature linking trait EI and coping). Items were rated on a 4-point Likert scale from 1 (“I haven’t been doing this at all”) to 4 (“I’ve been doing this a lot”). The Brief COPE has been validated in Arabic-speaking populations [39]. In the present sample, internal consistency was acceptable (α = 0.78; ⍵ = 0.83).
Sleep quality assessment used a single-item measure inspired by the well-established Pittsburgh Sleep Quality Index (PSQI; [40]). The measure was translated and back-translated by two bilingual Arabic–English researchers. Participants rated the overall quality of their sleep during the past two weeks (“How much do you rank the quality of your sleep in the past two weeks from 0 to 10?”) from 0 (“extremely bad”) to 10 (“extremely good”). Higher scores reflected better sleep. Single-item ratings of sleep quality have shown adequate reliability and strong convergent validity with established sleep instruments, supporting their use as valid indicators of subjective sleep perception in both adolescent and adult populations [41, 42]. These brief measures are particularly useful in studies where minimizing participant burden is important.
The post traumatic stress disorder checklist for DSM-5 short form (PCL-5 SF) [43], is a self-report instrument to assess PTSD symptoms associated with the most troubling event. It consists of 4 items rated from 0 (“not at all”) to 4 (“extremely”). The scale has been validated among Arabic-speaking populations [44]. In the present study, internal consistency was α = 0.74 and ⍵ = 0.77.
Posttraumatic growth inventory—short form (PTGI-SF) [45], captures positive psychological changes following traumatic experiences, across five domains: personal strength, spiritual change, new possibilities, appreciation of life, and relating to others. Participants rated 8 items from 0 (“I did not experience this change”) to 5 (“I experienced this change to a very great degree”). The PTGI-SF has shown excellent internal consistency and has been validated among Arabic-speaking adolescents [46]. In our sample, reliability indices reflected good internal consistency (α = 80; ⍵ = 84).
Data analysis
All statistical analyses were conducted using the R Statistical Software (v4.5.1; R Core Team 2024). Nine coding errors were found on the data during the initial analysis and declared as missing observations. An average of 34 (5.68% of the sample on average) observations were missing in the analysed variables, more detail can be found on Appendix Table 3. As a result, Little's MCAR test was applied [47] yielding a non-significant result (χ2 (2261) = 2349, p = 0.096), which suggested that the missingness was consistent with MCAR. K-nearest neighbours (k-NN) imputation was therefore applied [48, 49], as it is considered a robust non-parametric method for imputing both continuous and ordinal data [50, 51]. A sensitivity analysis was performed to assess the main findings without imputed data by fitting models with the FIML estimation method [52, 53], see Appendix Tables 4 to 9 for more information. Next, means, standard deviations, median absolute deviations, minimum and maximum values of the scales and items were obtained. Afterwards, Cronbach’s alpha [54] and McDonald’s omega [55] were obtained. Then Pearson correlation coefficients between the scales were computed to obtain descriptive associations among scales.
Structural equation modelling (SEM) was selected to analyze the different relationships between variables and to test the proposed hypothesis [56]. Prior to the SEM modelling, the multivariate normality and the multicollinearity assumption were tested [57–59]; see Appendix Tables 10 and 11 for more detail. Based on these tests, the MLR estimator with Huber-White’ robust standard errors and the Yuan-Bentler T2-star test statistic were used [60, 61]. To assess the fit of the model the Robust CFI and Robust TLI with the 0.95 cut-off value [62] were used as well as the Robust RMSEA and the SRMR with their respective 0.06 and 0.08 thresholds [63].
Results
Descriptive statistics
The means and Median Absolute Deviations (MAD) of the scales used in the model were: Adaptive coping showed a mean of 22.61 (MAD = 4.45), PCL-5 had a mean score of 7.44 (MAD = 4.45), PTGI had a moderate score with a mean of 29.92 (MAD = 8.9) and trait EI also had a moderate value in its mean of 17.8 (MAD = 3.68). The mean Sleep Quality in the sample was 4.83 (SD = 2.29).
Correlations among study variables
The Pearson correlation coefficients of the scales in the model are displayed in Table 1.
Table 1.
Pearson correlation coefficients of the variables in the model
| Sleep quality | Adaptive coping | PCL-5 | PTGI | Trait EI | |
|---|---|---|---|---|---|
| Sleep quality | 1 | ||||
| Adaptive coping | 0.231*** | 1 | |||
| PCL-5 | − 0.321*** | − 0.146*** | 1 | ||
| PTGI | 0.068 | 0.509*** | − 0.128** | 1 | |
| Trait EI | 0.312*** | 0.458*** | − 0.605*** | 0.485*** | 1 |
***p < 0.001; *p < 0.01
Trait EI was positively correlated with Sleep Quality, Adaptive coping and PTGI. Conversely, Trait EI was negatively correlated to PCL-5.
On the outcome variables side, PTGI was not correlated with Sleep Quality but was highly correlated with Adaptive coping. On the other hand, PCL-5 was negatively correlated with both Sleep Quality and Adaptive coping. Both outcome variables were negatively correlated between themselves.
Finally, the mediator variables Adaptive coping and Sleep Quality were positively correlated.
Structural equation modeling (SEM)
Measurement model
Before examining the structural relationships between the study variables, the measurement model was evaluated to ensure that the latent constructs were adequate (Robust CFA = 0.934, Robust TLI = 0.923, Robust RMSEA [90% CI] = 0.04 [0.039, 0.050], SRMR = 0.05, χ2(238) = 496.45, p < 0.001). Both the Robust CFA and Robust TLI were slightly below the traditional cut-offs [62] but the values of the Robust RMSEA and SRMR were well below the thresholds [63] pointing towards a satisfactory global fit. Furthermore, all the standardized loadings of the items in the model displayed values between 0.36 and 0.8 (p < 0.001) (Appendix, Table 12).
Structural model and hypothesis testing
After checking the fit of the measurement model, structural paths were added to test the hypothesis, see Fig. 1. The overall fit of the model remained largely unchanged even with the addition of Sleep Quality to the model: χ2(259) = 532.11, p < 0.001, RMSEA = 0.04 [90% CI: 0.038, 0.049], SRMR = 0.05, Robust CFI = 0.932, Robust TLI = 0.921. Composite reliability (CR) values were acceptable across the latent constructs, ranging from 0.711 to 0.801. Average variance extracted (AVE) values ranged from 0.280 to 0.487, indicating acceptable composite reliability but modest convergent validity for some constructs, particularly adaptive coping and PTGI (Appendix, Tables 13 to 14).
Fig. 1.
Structural equation model showing the relationships between the study variables (For clarity purposes, no correlations among manifest variables nor indirect effects are depicted in the figure.), (Values are standardized estimates. The standardized Trait EI to PCL-5 path coefficient exceeded |1| and is interpreted cautiously as a feature of the standardized solution under suppression-like adjustment and high explained variance. The corresponding unstandardized estimate is reported in Table 2.)
As seen in Table 2, trait EI positively was associated with Sleep Quality with the standardized estimate of 0.37 pointing towards a moderate strength relationship. Trait EI also was significantly associated with adaptive coping, with the standardized estimate of 0.58 outlining an even stronger relationship. In addition, trait EI was also significantly related to both outcomes, showing a positive relationship with PTGI and a negative relationship with PCL-5. The standardized estimate of 0.45 was of moderate strength for the PTGI and trait EI relation but of an abnormal high negative strength for the PCL-5 relation with a value of − 1.08, and was therefore interpreted cautiously rather than as a conventional standardized effect-size estimate.
Table 2.
Estimates of the SEM structural model
| Endogenous variables | Exogenous variables | Estimate | 95% CI of the estimates | Standardized estimates | p-value | SMC | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (A) Regression weights | |||||||
| Sleep quality | Trait EI | 0.79 | 0.60 | 0.99 | 0.37 | < 0.001 | 0.14 |
| Adaptive coping | Trait EI | 0.29 | 0.22 | 0.35 | 0.58 | < 0.001 | 0.33 |
| PTGI | Adaptive coping | 0.90 | 0.61 | 1.20 | 0.43 | < 0.001 | 0.56 |
| Trait EI | 0.47 | 0.33 | 0.61 | 0.45 | < 0.001 | ||
| Sleep quality | − 0.07 | − 0.11 | − 0.04 | − 0.15 | < 0.001 | ||
| PCL5 | Adaptive coping | 0.68 | 0.46 | 0.91 | 0.46 | < 0.001 | 0.87 |
| Trait EI | − 0.79 | − 0.95 | − 0.64 | − 1.08 | < 0.001 | ||
| Sleep quality | − 0.03 | − 0.06 | 0.00 | − 0.10 | 0.035 | ||
| Antecedent variables | Mediator variables | Outcome variables | Estimate | 95% CI of the estimates | Standardized estimates | p-value | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (B) Indirect effects | |||||||
| Trait EI | Sleep quality | PTGI | − 0.06 | − 0.09 | − 0.02 | − 0.06 | 0.001 |
| Trait EI | Adaptive coping | PTGI | 0.26 | 0.17 | 0.35 | 0.25 | < 0.001 |
| Trait EI | Sleep quality-adaptive coping | PTGI | 0.20 | 0.10 | 0.30 | 0.19 | < 0.001 |
| Trait EI | Sleep quality | PCL5 | − 0.03 | − 0.05 | 0.00 | − 0.04 | 0.028 |
| Trait EI | Adaptive coping | PCL5 | 0.20 | 0.11 | 0.28 | 0.27 | < 0.001 |
| Trait EI | Sleep quality-adaptive coping | PCL5 | 0.17 | 0.08 | 0.26 | 0.23 | < 0.001 |
To evaluate whether this estimate was primarily attributable to multicollinearity [64], additional collinearity diagnostics were conducted for the predictors entering the PTS equation. Using factor-score proxies, VIFs ranged from 1.18 to 1.89, tolerance values ranged from 0.53 to 0.84, and the maximum condition index was 4.90; see Appendix Table 15 to 17 for more detail. These diagnostics did not indicate problematic multicollinearity. Latent correlations further indicated that the strongest association was between trait EI and PTS, rather than among the predictors themselves.
Follow-up models suggested a suppression-like pattern. When trait EI was entered as the only predictor of PCL-5, the standardized trait EI to PCL-5 path coefficient was β = − 0.784. After adaptive coping was added to the equation, the standardized coefficient increased in magnitude to β = − 1.105. Accordingly, the standardized coefficient exceeding |1| appears to reflect a feature of the standardized solution under suppression-like adjustment, rather than evidence of severe multicollinearity; for more detail see Appendix Table 18. For this reason, interpretation of this path should emphasize the unstandardized estimate.
Regarding the influence of Adaptive coping in the outcome variables, Adaptive coping was positively related to PTGI as well as to PCL-5. The standardized estimates of Adaptive coping to both outcome variables were very similar (PTGI was 0.43 and PCL-5 was 0.46). Sleep Quality had the opposite effect of Adaptive coping on PTGI, higher values of Sleep Quality tended to have a negative effect on PTGI. This result led to a subsequent partial correlation analysis that suggested a suppression effect: after controlling for Trait Emotional Intelligence and Adaptive Coping, Sleep Quality was negatively associated with PTGI (r = − 0.14, p < 0.001).
The association between PTGI and PCL-5 also differed across levels of analysis. At the observed-score level, PTGI and PCL-5 were negatively correlated in Table 1. However, the SEM indicated a positive residual association between the latent outcome variables after adjustment for shared predictors, r = 0.65, p < 0.001. This suggests that post-traumatic growth and post-traumatic stress may co-occur conditionally within the multivariable model rather than functioning as simple opposites.
As for the indirect effect of trait EI on the outcome variables through Adaptive coping and Sleep Quality, the aggregate effect of trait EI on PTGI was positive (Estimate = 0.2, 95% CI [0.1, 0.3], p < 0.001) albeit somewhat low, with a standardized estimate of 0.192. When the aggregate effect of trait EI on PTGI was broken down, the indirect effect through Sleep Quality was negative but very low, with a standardized estimate of − 0.06. The indirect effect though Adaptive coping to PTGI was higher with a standardized value of 0.25 suggesting a low to medium effect.
The indirect effects of trait EI on PCL-5 followed a similar pattern as with PTGI. On the aggregate level the relationship was positive and significant, but with a standardized estimate of 0.23, not very strong. When disaggregated the indirect effect through Sleep Quality was negative, barely significant, and very low, with a standardized estimate of − 0.035. The indirect effect through Adaptive coping to PCL-5 followed the previous pattern of PTGI, a positive relation with a standardized value of 0.265 suggesting a similar low to medium effect.
Overall, the amount of explained variance of the outcome variables in the model was very satisfactory, with 87% of the variance of PCL-5 explained and 56% of PTGI both through the indirect and direct effects that Trait EI, Adaptive coping and Sleep Quality had on them. To a lower extent, 33% of the variance of Adaptive coping was explained by Trait EI. And only 14% of Sleep Quality variance was explained by Trait EI.
Discussion
This study examined the mediating role of adaptive coping strategies and sleep quality on the relationship between trait EI and two trauma-related outcomes, PTG and PTS among Lebanese youth. Emotional Intelligence was robustly associated with trauma-related outcomes, showing both direct and indirect associations with posttraumatic growth and posttraumatic stress symptoms through adaptive coping and sleep quality. Adaptive coping was found to play a meditative role in the relationship that links trait EI to both positive and negative trauma outcomes, whereas sleep quality pointed towards a weaker, suppressive mediating role. Overall, the model explained substantial variance in trauma outcomes in adolescents, underscoring the central role of emotional self-regulatory capacities in post-trauma adjustment in this vulnerable age-group.
First and foremost, results are consistent with our first hypothesis as adolescents with high trait EI demonstrated higher PTG and lower PTS symptoms, suggesting a protective role of trait EI in navigating adversity. This is in line with previous research similarly showing greater growth and lower stress [28] post-trauma in those with higher trait EI. We also found that adaptive coping was positively associated with PTG, which supports the concept that coping mechanisms such as acceptance, active coping, and positive reframing facilitate growth after trauma. Yet adaptive coping was also counterintuitively positively associated with PTS in our study, as results indicate individuals with higher PTS were most actively engaging in coping strategies. Even though adaptive coping generally co-occurs with lower distress, this finding reflects the dual nature of trauma adaptation instead of challenging our overall structural model. Adolescents experiencing higher distress could be mobilizing coping resources more extensively, since prior research has reported that strategies like emotion-focused coping lead to higher levels of PTS symptoms [65]. Recent theoretical frameworks of trauma further argue that growth does not emerge from the absence of distress but rather from active emotional engagement [3]. In the Lebanese context, adaptive coping may be a necessary, high-effort response to severe ongoing compounding stressors. This finding, taken with the positive correlation between PTS and PTG, suggests that adolescents who engage in active coping may simultaneously be more in touch with the trauma or its associated cues and would subsequently report more distress and growth. Adaptive coping strategies to protracted struggle could thus worsen a short-term distress for a longer-term catalytic effect on growth. PTG and PTS would be viewed as part of the same matching adaptation processes rather than opposing trauma outcomes [4]. In this model, coping would not merely function as a tool that decreases PTS, but as a meaning-making tool that co-occurs with distress and ultimately facilitates growth.
Interestingly, the strong positive association observed between PTG and PTS in adolescents further supports contemporary perspectives that growth and distress may co-occur particularly in highly traumatized populations [28]. The foundational theory of PTG in fact argues that growth only emerges through engagement with traumatic distress, not merely its absence [6, 66]. Cognitive and emotional struggles would associate with distress and growth similarly especially in young developmental ages [67]. Moreover, we found that while improved sleep quality was associated with fewer PTS symptoms, it unexpectedly showed a negative association with PTG. Complex dynamics between coping processes and psychological outcomes might account for this finding. Although reinforcing the role of sleep as a tool for emotional regulation and diminished distress like in previous studies [23], the partial correlation analyses in our sample indicated a potential suppression effect of sleep on growth. Better sleep shows a total positive covariation with variables that foster growth (EI and adaptive coping), yet, once those variables are controlled, the unique variance in sleep quality inversely relates to PTG. This would mean that while sleep is restorative and essential for emotional regulation and reducing PTS symptoms, it may be insufficiently constructive for the advanced cognitive processing that defines PTG [46]. In the literature, improved sleep quality has indeed been associated with higher wellbeing, but direct evidence of its impact on growth remains limited [68]. Additionally, better sleep quality was associated with a more “passive” subjective perception of better emotional functioning, but was unrelated to the “active” performance-based metrics of emotional reasoning [69], sustaining the more elaborate aspect of PTG. Yet, recent clinical research has further underscored that sleep interventions can aid in the attenuation of hyperarousal that is common in PTSD and may gradually pave the way for cognitive processing and growth [70].
In this model trait EI promotes regulation via adaptive coping and sleep, thereby shaping trauma outcomes. Trait EI did not only show direct effects on both PTS and PTG, highlighting its relevance as a dispositional resource post-trauma; our advanced modelling analyses show that it additionally influenced trauma outcomes indirectly through adaptive coping and sleep quality. Adolescents with higher trait EI were more likely to employ constructive coping strategies and had healthier sleep quality in the aftermath of adversity. These findings align with prior research demonstrating that trait EI enhances individuals’ internal resources, enabling them to manage stress more effectively and linking trait EI to improved overall sleep quality [33]. Theoretical models position trait EI indeed as a foundational capacity that facilitates flexible, goal-directed coping under stress, thereby shaping downstream psychological outcomes following trauma exposure [71]. In our novel integrative model, EI strongly is related to adaptive coping strategies, which in turn were positively associated with PTG and, more modestly, with PTS. This is in line with accumulating evidence that shows that coping processes mediate links between trait EI and positive psychological outcomes, such as wellbeing or reduced distress [18]. It thus becomes crucial to help adolescents foster awareness of their post-trauma distress symptoms while equipping them with coping skills to positively influence trauma-related outcomes and support their continued progression towards PTG [46]. Sleep however, had a modest effect in mediating the relationship between trait EI and trauma outcomes. Sleep quality has previously been studied in correlation with higher trait EI [72] and has been associated with positive mental health, such as higher wellbeing and lower risks of psychological and psychiatric distress [73]. The overall explanatory power of the model was substantial, whereby sleep accounts for a large proportion of variance in PTS symptoms and a meaningful proportion in PTG, thereby reinforcing the value of integrative models that jointly consider emotional traits, adaptive coping processes, and restorative behaviors like sleep when investigating post-trauma outcomes. From the perspective of the Polyvagal Theory of Trauma [74], these novel findings stipulate that greater autonomic activation post-trauma could increase both PTS and PTG, explaining their positive link, while sleep would reflect decreased threat activation that dampens both.
In Lebanon, sleep disturbances are highly prevalent in children and adolescents and recent studies have shown they are rather easily modifiable with factors like diet and sleep hygiene to better capitalize on sleep therapeutic benefits [75]. In this context, sleep quality becomes an important tool to offset negative trauma impact especially in a country with pervasive stigma around mental health and mental health support [74]. Adaptive coping and sleep quality could thus be considered complementary but asymmetric mediators of the relationship between trait EI and trauma outcomes in adolescents. Our model mostly highlights the complexities of adolescent adaptation in high-trauma environments.
Limitations, implications, and future recommendations
While this study yielded valuable insights, several limitations need to be acknowledged. A primary limitation concerns the sample as participants were drawn exclusively from public schools in Lebanon, which restricts generalizability of the findings to other educational systems and socio-economic contexts. Moreover, Lebanon’s unique sociopolitical instability and community-oriented coping traditions may shape coping and PTG in ways that differ from other cultural or geopolitical settings. Moving forward, future research could build on these findings by employing longitudinal designs to clarify developmental and causal pathways; expanding sampling to include private schools, vocational settings, and diverse cultural contexts; and integrating multi-informant methods to complement self-reported data (e.g., teacher and parent reports) to provide a more balanced assessment.
Additionally, this research lacked a systematic categorisation of trauma types, as trauma exposure was assessed in relation to distressing events occurring within the broader Lebanese sociopolitical context. Future research could involve observing the behaviour of Lebanese public school students to address the potential limitations of self-rated scales more effectively. This could include a detailed trauma typology to categorise trauma types systematically.
Another limitation concerns the reliance on self-report instruments, which increases the risk of social desirability bias. Although the tools were culturally adapted, participants’ responses may not fully capture the nuances of how sleep quality, coping, PTG, and PTS symptoms are expressed in Lebanese adolescents. In addition, the study’s cross-sectional design precludes causal interpretations. While SEM provides valuable directional insight, it does not allow for firm conclusions about temporal sequencing or causality.
Furthermore, although our advanced mediation analyses were statistically supported in the SEM model, other variables might play a role in shaping the complex relationships examined. Additionally, the study did not directly assess the type, severity, or cumulative nature of trauma exposure, potentially constraining the precision of interpretations regarding differential exposure effects and underscoring the importance of incorporating detailed trauma assessments in future research.
Conclusion and clinical implications
This study provides significant contributions as it strengthens the theoretical model that trait EI is a vital psychological resource associated with trauma outcomes in the aftermath of collective adversities in adolescents. By examining the mediating role of adaptive coping and sleep within a Middle Eastern, trauma-exposed sample, our findings expand the scope of trait EI research underrepresented regions.
On a practical level, the results underscore the value of embedding trauma-informed EI and coping skills within school-based psychosocial programs, particularly in contexts marked by chronic instability. Initiatives such as YEY illustrate how equipping adolescents with emotion regulation coping skills and behavioral adjustments of sleep quality can buffer distress and foster growth [28]. These insights guide educators, practitioners, and policymakers seeking scalable, preventive approaches to support youth resilience and mental health. They also suggest that clinical interventions could target sleep hygiene protocols for symptom reduction and might pair it with EI-based cognitive training to ensure that post-trauma symptom recovery is matched by psychological growth.
Acknowledgements
The authors would like to express their sincere gratitude to Monah AlHassanieh, Perla Zgheib, and Peter Wanna for their invaluable support in the data collection process and their contributions to the successful completion of the project that led to this study.
Appendix
See Tables 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18.
Table 3.
Missing cases of variables on the models
| Variable | Absolute | Percentage |
|---|---|---|
| COPE_1 | 8 | 1.31 |
| COPE_6 | 14 | 2.29 |
| COPE_10 | 10 | 1.63 |
| COPE_12 | 18 | 2.94 |
| COPE_15 | 11 | 1.80 |
| COPE_16 | 13 | 2.12 |
| COPE_18 | 8 | 1.31 |
| COPE_19 | 11 | 1.80 |
| PCL_1 | 11 | 1.80 |
| PCL_2 | 6 | 0.98 |
| PCL_3 | 5 | 0.82 |
| PCL_4 | 7 | 1.14 |
| PTGI_2 | 73 | 11.93 |
| PTGI_3 | 57 | 9.31 |
| PTGI_4 | 71 | 11.60 |
| PTGI_6 | 58 | 9.48 |
| PTGI_7 | 55 | 8.99 |
| PTGI_8 | 55 | 8.99 |
| PTGI_9 | 73 | 11.93 |
| PTGI_10 | 55 | 8.99 |
| Wellbeing | 48 | 7.84 |
| Selfcontrol | 47 | 7.68 |
| Emotionality | 63 | 10.29 |
| Sociability | 57 | 9.31 |
Table 4.
Goodness of Fit Indices of the SEM measurement model using FIML
| Model | χ2 | df | Robust RMSEA | 90% CI | SRMR | Robust CFI | Robust TLI |
|---|---|---|---|---|---|---|---|
| SEM model | 487.37 | 238 | 0.05 | 0.04–0.052 | 0.05 | 0.929 | 0.918 |
Table 5.
Loadings of the latent variables in the SEM measurement model using FIML
| Latent variables | Measurement variables | Loadings | 95% CI of the loadings | Standardized estimates | p-value | SMC | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (A) Estimates of loadings | |||||||
| Adaptive coping | COPE_1 | 1 | 1.00 | 1.00 | 0.60 | – | 0.36 |
| COPE_6 | 1.02 | 0.86 | 1.18 | 0.58 | < 0.001 | 0.34 | |
| COPE_10 | 0.87 | 0.66 | 1.09 | 0.47 | < 0.001 | 0.22 | |
| COPE_12 | 1.05 | 0.87 | 1.23 | 0.60 | < 0.001 | 0.36 | |
| COPE_15 | 0.92 | 0.73 | 1.12 | 0.53 | < 0.001 | 0.28 | |
| COPE_16 | 0.67 | 0.47 | 0.87 | 0.37 | < 0.001 | 0.13 | |
| COPE_18 | 0.84 | 0.64 | 1.03 | 0.51 | < 0.001 | 0.26 | |
| COPE_19 | 0.99 | 0.79 | 1.18 | 0.56 | < 0.001 | 0.32 | |
| PCL5 | PCL_1 | 1.00 | 1.00 | 1.00 | 0.61 | – | 0.37 |
| PCL_2 | 0.90 | 0.75 | 1.05 | 0.52 | < 0.001 | 0.27 | |
| PCL_3 | 1.31 | 1.12 | 1.50 | 0.71 | < 0.001 | 0.50 | |
| PCL_4 | 1.27 | 1.08 | 1.45 | 0.70 | < 0.001 | 0.48 | |
| PTGI | PTGI_2 | 1.00 | 1.00 | 1.00 | 0.72 | – | 0.52 |
| PTGI_3 | 0.92 | 0.82 | 1.03 | 0.70 | < 0.001 | 0.48 | |
| PTGI_4 | 0.58 | 0.44 | 0.73 | 0.41 | < 0.001 | 0.17 | |
| PTGI_6 | 0.82 | 0.68 | 0.96 | 0.56 | < 0.001 | 0.31 | |
| PTGI_7 | 0.90 | 0.76 | 1.03 | 0.66 | < 0.001 | 0.44 | |
| PTGI_8 | 0.54 | 0.41 | 0.67 | 0.42 | < 0.001 | 0.18 | |
| PTGI_9 | 0.84 | 0.71 | 0.98 | 0.66 | < 0.001 | 0.44 | |
| PTGI_10 | 0.67 | 0.55 | 0.80 | 0.48 | < 0.001 | 0.23 | |
| Trait EI | Wellbeing | 1.00 | 1.00 | 1.00 | 0.80 | – | 0.65 |
| Selfcontrol | 0.82 | 0.71 | 0.92 | 0.70 | < 0.001 | 0.50 | |
| Emotionality | 0.55 | 0.48 | 0.63 | 0.64 | < 0.001 | 0.41 | |
| Sociability | 0.54 | 0.45 | 0.63 | 0.56 | < 0.001 | 0.31 | |
| Measurement variables | Covariances | 95% CI of the covariances | Correlations | p-value | ||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| (B) Covariances of manifest variables | ||||||
| COPE_16 | COPE_18 | 0.21 | 0.14 | 0.28 | 0.31 | < 0.001 |
| COPE_10 | COPE_15 | 0.18 | 0.10 | 0.25 | 0.25 | < 0.001 |
| COPE_16 | COPE_19 | 0.11 | 0.04 | 0.18 | 0.16 | 0.002 |
| COPE_18 | COPE_19 | 0.09 | 0.03 | 0.16 | 0.16 | 0.002 |
| PCL_1 | PCL_2 | 0.18 | 0.05 | 0.32 | 0.15 | 0.006 |
| COPE_1 | COPE_6 | 0.11 | 0.05 | 0.18 | 0.21 | 0.001 |
| Emotionality | Sociability | 0.11 | 0.05 | 0.17 | 0.17 | 0.001 |
| Selfcontrol | Emotionality | 0.12 | 0.04 | 0.19 | 0.19 | 0.002 |
EI, Emotional Intelligence
Table 6.
Goodness of Fit Indices of the SEM structural model using FIML
| Model | χ2 | df | Robust RMSEA | 90% CI | SRMR | Robust CFI | Robust TLI |
|---|---|---|---|---|---|---|---|
| SEM model | 521.16 | 259 | 0.05 | 0.04–0.051 | 0.05 | 0.928 | 0.917 |
Table 7.
Loadings of the latent variables in the SEM structural model using FIML
| Latent variables | Measurement variables | Loadings | 95% CI of the loadings | Standardized estimates | p-value | SMC | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (A) Estimates of loadings | |||||||
| Adaptive coping | COPE_1 | 1 | 1.00 | 1.00 | 0.60 | – | 0.36 |
| COPE_6 | 1.02 | 0.86 | 1.18 | 0.59 | < 0.001 | 0.34 | |
| COPE_10 | 0.87 | 0.66 | 1.08 | 0.47 | < 0.001 | 0.22 | |
| COPE_12 | 1.03 | 0.86 | 1.21 | 0.60 | < 0.001 | 0.36 | |
| COPE_15 | 0.92 | 0.72 | 1.11 | 0.53 | < 0.001 | 0.28 | |
| COPE_16 | 0.66 | 0.46 | 0.86 | 0.36 | < 0.001 | 0.13 | |
| COPE_18 | 0.83 | 0.64 | 1.02 | 0.51 | < 0.001 | 0.26 | |
| COPE_19 | 0.98 | 0.79 | 1.17 | 0.56 | < 0.001 | 0.32 | |
| PCL5 | PCL_1 | 1.00 | 1.00 | 1.00 | 0.61 | – | 0.37 |
| PCL_2 | 0.91 | 0.76 | 1.06 | 0.52 | < 0.001 | 0.28 | |
| PCL_3 | 1.31 | 1.13 | 1.49 | 0.70 | < 0.001 | 0.49 | |
| PCL_4 | 1.29 | 1.11 | 1.48 | 0.70 | < 0.001 | 0.50 | |
| PTGI | PTGI_2 | 1.00 | 1.00 | 1.00 | 0.72 | – | 0.51 |
| PTGI_3 | 0.93 | 0.82 | 1.03 | 0.69 | < 0.001 | 0.48 | |
| PTGI_4 | 0.59 | 0.45 | 0.74 | 0.42 | < 0.001 | 0.17 | |
| PTGI_6 | 0.82 | 0.69 | 0.96 | 0.56 | < 0.001 | 0.32 | |
| PTGI_7 | 0.90 | 0.77 | 1.04 | 0.66 | < 0.001 | 0.44 | |
| PTGI_8 | 0.55 | 0.42 | 0.68 | 0.43 | < 0.001 | 0.18 | |
| PTGI_9 | 0.85 | 0.72 | 0.98 | 0.67 | < 0.001 | 0.44 | |
| PTGI_10 | 0.68 | 0.55 | 0.81 | 0.49 | < 0.001 | 0.24 | |
| Trait EI | Wellbeing | 1.00 | 1.00 | 1.00 | 0.80 | – | 0.64 |
| Selfcontrol | 0.83 | 0.72 | 0.93 | 0.71 | < 0.001 | 0.50 | |
| Emotionality | 0.56 | 0.48 | 0.63 | 0.64 | < 0.001 | 0.41 | |
| Sociability | 0.54 | 0.45 | 0.63 | 0.55 | < 0.001 | 0.30 | |
| Measurement variables | Covariances | 95% CI of the covariances | Correlations | p-value | ||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| (B) Covariances of manifest variables | ||||||
| COPE_16 | COPE_18 | 0.21 | 0.14 | 0.28 | 0.31 | < 0.001 |
| COPE_10 | COPE_15 | 0.18 | 0.10 | 0.25 | 0.25 | < 0.001 |
| COPE_16 | COPE_19 | 0.11 | 0.04 | 0.18 | 0.16 | 0.002 |
| COPE_18 | COPE_19 | 0.10 | 0.03 | 0.16 | 0.16 | 0.002 |
| PCL_1 | PCL_2 | 0.19 | 0.06 | 0.32 | 0.16 | 0.004 |
| COPE_1 | COPE_6 | 0.11 | 0.04 | 0.18 | 0.20 | 0.001 |
| Emotionality | Sociability | 0.11 | 0.05 | 0.17 | 0.18 | < 0.001 |
| Selfcontrol | Emotionality | 0.12 | 0.04 | 0.19 | 0.19 | 0.002 |
EI, Emotional Intelligence
Table 8.
Covariances and correlations of the SEM structural model using FIML
| Exogenous variables | Covariances | 95% CI of the covariances | Correlations | p-value | ||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| (A) Covariances of exogenous variables | ||||||
| PCL5 | PTGI | 0.16 | 0.08 | 0.23 | 0.70 | < 0.001 |
Table 9.
Estimates of the SEM structural model using FIML
| Endogenous variables | Exogenous variables | Estimate | 95% CI of the estimates | Standarized estimates | p-value | SMC | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (A) Regression weights | |||||||
| Sleep quality | Trait EI | 0.81 | 0.61 | 1.00 | 0.37 | < 0.001 | 0.14 |
| Adaptive coping | Trait EI | 0.29 | 0.22 | 0.36 | 0.58 | < 0.001 | 0.34 |
| PTGI | Adaptive coping | 0.93 | 0.62 | 1.25 | 0.43 | < 0.001 | 0.58 |
| Trait EI | 0.51 | 0.35 | 0.67 | 0.47 | < 0.001 | ||
| Sleep Quality | − 0.09 | − 0.13 | − 0.05 | − 0.18 | < 0.001 | ||
| PCL5 | Adaptive coping | 0.69 | 0.45 | 0.93 | 0.47 | < 0.001 | 0.86 |
| Trait EI | − 0.80 | − 0.97 | − 0.64 | − 1.08 | < 0.001 | ||
| Sleep Quality | − 0.03 | − 0.06 | 0.00 | − 0.09 | 0.055 | ||
| Antecedent variables | Mediator variables | Outcome variables | Estimate | 95% CI of the estimates | Standarized estimates | p-value | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (B) Indirect effects | |||||||
| Trait EI | Sleep Quality | PTGI | − 0.07 | − 0.11 | − 0.03 | − 0.07 | 0.001 |
| Trait EI | Adaptive coping | PTGI | 0.27 | 0.17 | 0.37 | 0.25 | < 0.001 |
| Trait EI | Sleep Quality-Adaptive coping | PTGI | 0.20 | 0.09 | 0.30 | 0.18 | < 0.001 |
| Trait EI | Sleep Quality | PCL5 | − 0.03 | − 0.05 | 0.00 | − 0.03 | 0.044 |
| Trait EI | Adaptive coping | PCL5 | 0.20 | 0.11 | 0.29 | 0.27 | < 0.001 |
| Trait EI | Sleep Quality-Adaptive coping | PCL5 | 0.18 | 0.08 | 0.27 | 0.24 | 0.001 |
Table 10.
Regression results of the multicollinearity tests
| Tests | Beta-hat | kappa | p-value |
|---|---|---|---|
| Mardia | |||
| Skewness | 38.69 | 3946.03 | < 0.001 |
| Kurtosis | 684.44 | 21.16 | < 0.001 |
| Replicates | E-statistic | p-value | |
| Energy | 10,000 | 3.8668 | < 0.001 |
Table 11.
Regression results of the multicollinearity tests
| Table C1. Regression results of the multicollinearity tests | ||
|---|---|---|
| Variables | Coefficients | VIFs |
| (A) Coefficients and VIFs of the model with Adaptive coping variables | ||
| Intercept | 0.58 | – |
| COPE_1 | − 0.01 | 1.50 |
| COPE_6 | 0.01 | 1.42 |
| COPE_10 | 0.01 | 1.34 |
| COPE_12 | 0.01 | 1.41 |
| COPE_15 | − 0.01 | 1.40 |
| COPE_16 | 0.00 | 1.29 |
| COPE_18 | − 0.02 | 1.45 |
| COPE_19 | 0.00 | 1.41 |
| (B) Coefficients and VIFs of the models with PCL5 variables | ||
| Intercept | 0.52 | – |
| PCL_1 | − 0.03 | 1.44 |
| PCL_2 | 0.02 | 1.32 |
| PCL_3 | 0.01 | 1.50 |
| PCL_4 | 0.01 | 1.44 |
| (C) Coefficients and VIFs of the model with PTGI variables | ||
| Intercept | 0.50 | – |
| PTGI_2 | 0.02 | 1.70 |
| PTGI_3 | − 0.02 | 1.69 |
| PTGI_4 | 0.00 | 1.19 |
| PTGI_6 | 0.01 | 1.35 |
| PTGI_7 | − 0.01 | 1.52 |
| PTGI_8 | 0.02 | 1.22 |
| PTGI_9 | − 0.01 | 1.60 |
| PTGI_10 | − 0.02 | 1.27 |
| (D) Coefficients and VIFs of the model with Trait EI variables | ||
| Intercept | 0.38 | – |
| Wellbeing | − 0.01 | 1.74 |
| Selfcontrol | 0.00 | 1.66 |
| Emotionality | 0.06 | 1.66 |
| Sociability | 0.00 | 1.41 |
Table 12.
Loadings of the latent variables in the SEM measurement model
| Latent variables | Measurement variables | Loadings | 95% CI of the loadings | Standardized estimates | p-value | SMC | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (A) Estimates of loadings | |||||||
| Adaptive coping | COPE_1 | 1 | 1.00 | 1.00 | 0.60 | – | 0.36 |
| COPE_6 | 1.00 | 0.84 | 1.16 | 0.57 | < 0.001 | 0.33 | |
| COPE_10 | 0.88 | 0.66 | 1.09 | 0.47 | < 0.001 | 0.22 | |
| COPE_12 | 1.05 | 0.87 | 1.22 | 0.60 | < 0.001 | 0.36 | |
| COPE_15 | 0.94 | 0.74 | 1.13 | 0.53 | < 0.001 | 0.28 | |
| COPE_16 | 0.66 | 0.46 | 0.86 | 0.36 | < 0.001 | 0.13 | |
| COPE_18 | 0.85 | 0.65 | 1.04 | 0.51 | < 0.001 | 0.26 | |
| COPE_19 | 0.99 | 0.80 | 1.18 | 0.57 | < 0.001 | 0.32 | |
| PCL5 | PCL_1 | 1.00 | 1.00 | 1.00 | 0.61 | – | 0.37 |
| PCL_2 | 0.92 | 0.76 | 1.07 | 0.53 | < 0.001 | 0.28 | |
| PCL_3 | 1.33 | 1.14 | 1.53 | 0.71 | < 0.001 | 0.50 | |
| PCL_4 | 1.28 | 1.10 | 1.47 | 0.70 | < 0.001 | 0.49 | |
| PTGI | PTGI_2 | 1.00 | 1.00 | 1.00 | 0.72 | – | 0.51 |
| PTGI_3 | 0.92 | 0.82 | 1.02 | 0.69 | < 0.001 | 0.48 | |
| PTGI_4 | 0.59 | 0.45 | 0.72 | 0.41 | < 0.001 | 0.17 | |
| PTGI_6 | 0.83 | 0.70 | 0.96 | 0.56 | < 0.001 | 0.32 | |
| PTGI_7 | 0.91 | 0.78 | 1.04 | 0.66 | < 0.001 | 0.44 | |
| PTGI_8 | 0.55 | 0.42 | 0.68 | 0.42 | < 0.001 | 0.18 | |
| PTGI_9 | 0.84 | 0.72 | 0.97 | 0.66 | < 0.001 | 0.44 | |
| PTGI_10 | 0.68 | 0.56 | 0.80 | 0.48 | < 0.001 | 0.23 | |
| Trait EI | Wellbeing | 1.00 | 1.00 | 1.00 | 0.81 | – | 0.65 |
| Selfcontrol | 0.81 | 0.71 | 0.91 | 0.70 | < 0.001 | 0.49 | |
| Emotionality | 0.55 | 0.47 | 0.62 | 0.64 | < 0.001 | 0.40 | |
| Sociability | 0.53 | 0.45 | 0.62 | 0.55 | < 0.001 | 0.30 | |
| Measurement variables | Covariances | 95% CI of the covariances | Correlations | p-value | ||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| (B) Covariances of manifest variables | ||||||
| COPE_16 | COPE_18 | 0.20 | 0.14 | 0.27 | 0.30 | < 0.001 |
| COPE_10 | COPE_15 | 0.17 | 0.10 | 0.24 | 0.25 | < 0.001 |
| COPE_16 | COPE_19 | 0.11 | 0.04 | 0.18 | 0.16 | 0.002 |
| COPE_18 | COPE_19 | 0.10 | 0.04 | 0.15 | 0.17 | 0.002 |
| PCL_1 | PCL_2 | 0.18 | 0.06 | 0.31 | 0.15 | 0.005 |
| COPE_1 | COPE_6 | 0.11 | 0.05 | 0.17 | 0.20 | 0.001 |
| Emotionality | Sociability | 0.10 | 0.04 | 0.15 | 0.15 | 0.001 |
| Selfcontrol | Emotionality | 0.12 | 0.06 | 0.19 | 0.20 | < 0.001 |
EI, Emotional Intelligence
Table 13.
Loadings of the latent variables in the SEM structural model
| Latent variables | Measurement variables | Loadings | 95% CI of the loadings | Standardized estimates | p-value | SMC | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| (A) Estimates of loadings | |||||||
| Adaptive coping | COPE_1 | 1 | 1.00 | 1.00 | 0.60 | – | 0.36 |
| COPE_6 | 1.00 | 0.84 | 1.16 | 0.58 | < 0.001 | 0.33 | |
| COPE_10 | 0.87 | 0.67 | 1.08 | 0.47 | < 0.001 | 0.22 | |
| COPE_12 | 1.03 | 0.86 | 1.21 | 0.60 | < 0.001 | 0.36 | |
| COPE_15 | 0.93 | 0.74 | 1.12 | 0.53 | < 0.001 | 0.28 | |
| COPE_16 | 0.66 | 0.46 | 0.85 | 0.36 | < 0.001 | 0.13 | |
| COPE_18 | 0.84 | 0.65 | 1.03 | 0.51 | < 0.001 | 0.26 | |
| COPE_19 | 0.99 | 0.80 | 1.18 | 0.57 | < 0.001 | 0.32 | |
| PCL5 | PCL_1 | 1.00 | 1.00 | 1.00 | 0.60 | – | 0.36 |
| PCL_2 | 0.92 | 0.77 | 1.08 | 0.53 | < 0.001 | 0.28 | |
| PCL_3 | 1.33 | 1.15 | 1.52 | 0.71 | < 0.001 | 0.50 | |
| PCL_4 | 1.31 | 1.13 | 1.49 | 0.71 | < 0.001 | 0.50 | |
| PTGI | PTGI_2 | 1.00 | 1.00 | 1.00 | 0.71 | – | 0.51 |
| PTGI_3 | 0.92 | 0.82 | 1.03 | 0.69 | < 0.001 | 0.47 | |
| PTGI_4 | 0.59 | 0.46 | 0.73 | 0.42 | < 0.001 | 0.17 | |
| PTGI_6 | 0.83 | 0.70 | 0.96 | 0.56 | < 0.001 | 0.32 | |
| PTGI_7 | 0.91 | 0.78 | 1.04 | 0.66 | < 0.001 | 0.44 | |
| PTGI_8 | 0.56 | 0.43 | 0.68 | 0.43 | < 0.001 | 0.18 | |
| PTGI_9 | 0.85 | 0.72 | 0.97 | 0.67 | < 0.001 | 0.44 | |
| PTGI_10 | 0.69 | 0.56 | 0.81 | 0.49 | < 0.001 | 0.24 | |
| Trait EI | wellbeing | 1.00 | 1.00 | 1.00 | 0.80 | – | 0.64 |
| selfcontrol | 0.81 | 0.72 | 0.91 | 0.70 | < 0.001 | 0.49 | |
| emotionality | 0.55 | 0.48 | 0.62 | 0.63 | < 0.001 | 0.40 | |
| sociability | 0.53 | 0.45 | 0.62 | 0.54 | < 0.001 | 0.30 | |
| Measurement variables | Covariances | 95% CI of the covariances | Correlations | p-value | ||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| (B) Covariances of manifest variables | ||||||
| COPE_16 | COPE_18 | 0.20 | 0.14 | 0.27 | 0.30 | < 0.001 |
| COPE_10 | COPE_15 | 0.17 | 0.10 | 0.24 | 0.25 | < 0.001 |
| COPE_16 | COPE_19 | 0.11 | 0.04 | 0.18 | 0.16 | 0.002 |
| COPE_18 | COPE_19 | 0.10 | 0.04 | 0.15 | 0.17 | 0.002 |
| PCL_1 | PCL_2 | 0.19 | 0.06 | 0.31 | 0.16 | 0.003 |
| COPE_1 | COPE_6 | 0.11 | 0.04 | 0.17 | 0.20 | 0.001 |
| emotionality | sociability | 0.10 | 0.04 | 0.15 | 0.16 | 0.001 |
| selfcontrol | emotionality | 0.12 | 0.06 | 0.19 | 0.20 | < 0.001 |
EI, Emotional Intelligence
Table 14.
Composite reliability and average variance extracted on the latent variables of the structural model
| Latent construct | CR | AVE |
|---|---|---|
| Trait EI | 0.75 | 0.49 |
| Adaptive coping | 0.71 | 0.28 |
| PCL-5 | 0.72 | 0.42 |
| PTGI | 0.80 | 0.35 |
Table 15.
Tolerance and variance inflation factor
| Variables | Tolerance | VIF |
|---|---|---|
| Trait EI | 0.53 | 1.89 |
| Adaptive coping | 0.58 | 1.73 |
| Sleep Quality | 0.84 | 1.18 |
Table 16.
Eigenvalue and condition index
| Eigenvalue | Condition Index | intercept | Trait EI | Adaptive coping | Sleep quality |
|---|---|---|---|---|---|
| 1.91 | 1.00 | 0.04 | 0.00 | 0.00 | 0.04 |
| 1.66 | 1.07 | 0.00 | 0.16 | 0.17 | 0.00 |
| 0.35 | 2.33 | 0.00 | 0.72 | 0.83 | 0.00 |
| 0.08 | 4.90 | 0.96 | 0.13 | 0.00 | 0.95 |
Table 17.
Latent correlations of the variables in the model
| Trait EI | Adaptive coping | PCL-5 | PTGI | |
|---|---|---|---|---|
| Trait EI | 1 | |||
| Adaptive coping | 0.58 | 1 | ||
| PCL-5 | − 0.85 | − 0.18 | 1 | |
| PTGI | 0.64 | 0.66 | − 0.25 | 1 |
Table 18.
Progressively adjusted models examining the Trait EI to PCL-5 path coefficient
| Model | Predictor of PCL-5 | b | SE | z | p | β | PCL-5 R2 |
|---|---|---|---|---|---|---|---|
| Model 1: Trait EI only | Trait EI | − 0.54 | 0.048 | − 11.308 | < 0.001 | − 0.784 | 0.615 |
| Model 2: Trait EI + adaptive coping | Trait EI | − 0.802 | 0.084 | − 9.603 | < 0.001 | − 1.105 | 0.841 |
| Adaptive coping | 0.711 | 0.13 | 5.468 | < 0.001 | 0.474 | ||
| Model 3: Full model | Trait EI | − 0.794 | 0.079 | − 10.091 | < 0.001 | − 1.079 | 0.871 |
| Adaptive coping | 0.681 | 0.115 | 5.903 | < 0.001 | 0.46 | ||
| Sleep quality | − 0.033 | 0.016 | − 2.107 | 0.035 | − 0.096 |
Author contribution
M.M., R.D., MJ. SR., R. C. and C.M. wrote the main manuscript. I. S.R. and R.C. prepared the tables and figures. All authors reviewed the manuscript.
Funding
This research was funded by Grand Challenges Canada – Global Mental Health – NIHS; R-GMH-POC-2107-47498. M.J. Sanchez-Ruiz’s work was funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011039) and FSE (Invierte en tu futuro).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. The authors do not have the right to share any data information since the data is owned by a third-party organization, the Lebanese American University. Data can be shared upon a reasonable request from Ms. Perla Zgheib (perla.z.zgheib@gmail.com), Lecturer, Department of Social and Education Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon.
Declarations
Ethics approval and consent to participate
This study received administrative approval from the Lebanese Ministry of Education and Higher Education (MEHE) and was approved by the Institutional Review Board (LAU IRB; reference number: LAU.SAS.MJ1.28/Oct/2021). All participants and their parents approved the informed consent given by the Institutional review Board in accordance with the Declaration of Helsinki before filling the survey.
Consent to publish
Not applicable.
Clinical trial number
Not applicable.
Competing Interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. The authors do not have the right to share any data information since the data is owned by a third-party organization, the Lebanese American University. Data can be shared upon a reasonable request from Ms. Perla Zgheib (perla.z.zgheib@gmail.com), Lecturer, Department of Social and Education Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon.