Introduction

Researchers have long been fascinated by the possible relationship between higher intelligence and distress or psychopathology. As early as 1930, a short paper was published by Ralph White entitled “Note on the Psychopathology of Genius” (White, 1930). However, there has been a continued inconsistency regarding the findings with respect to this relationship. Thus, the question remains whether gifted individuals suffer more or less from distress. On the one hand, there is the hypothesis that giftedness indeed increases the vulnerability of individuals (resulting in more distress) (Neihart, 1998), but on the other hand some studies hypothesize that giftedness is a protective factor that increases resilience (resulting in less distress) (Kitano & Lewis, 2005). Most empirical studies related to this topic took place in children and adolescents. The number of studies conducted in gifted adults is very limited, but existing studies found evidence in favour of both the first and second hypothesis (Vötter & Schnell, 2019; Rajput et al., 2011). Personal characteristics (including personality traits) of a gifted individual could be one of the factors that helps determine whether giftedness makes a person more susceptible to distress or psychopathology or whether giftedness serves as a protective factor against distress (Neihart, 1998). A possible candidate in this regard is Sensory Processing Sensitivity (SPS).

Sensory Processing Sensitivity (SPS) refers to a person’s sensitivity to subtle (internal and external) stimuli, the depth and intensity with which these stimuli are processed and analysed, and the impact this has on emotional and physiological reactivity (Greven et al., 2019). Within the theoretical literature related to SPS the concept of Differential Susceptibility has received increasing attention (Pluess & Belsky, 2013). Differential Susceptibility assumes that highly sensitive individuals are more sensitive to both positive and negative environmental stimuli. In doing so, this model integrates both the Diathesis-Stress framework (a high degree of SPS leads to a stronger adverse impact of negative stimuli) and the Vantage Sensitivity framework (a high degree of SPS implies greater sensitivity to the beneficial effects of positive environmental stimuli). As the underlying assumption of the Diathesis-Stress framework is that individuals who score high on SPS respond more strongly to stimuli (especially negative stimuli), this could translate into more negative outcomes, such as physical and psychological distress.

Previous studies have shown that high SPS, as measured with the Highly Sensitive Person Scale (HSPS; Aron & Aron, 1997), was associated with higher levels of somatic symptoms, psychological distress, psychopathology, anxiety, and depression (Benham, 2006; Grimen & Diseth, 2016; Dinc et al., 2021; Bakker & Moulding, 2012; Yano et al., 2019). A recent study conducted in a large general population sample and using a more comprehensive measure of SPS, the Sensory Processing Sensitivity Questionnaire (SPSQ), found strong associations between high SPS, anxiety and physical symptoms. In addition, a significant positive relationship was found between a negative higher-order dimension of SPS, reflecting strong physiological and emotional responses to intense stimuli and the tendency to withdraw when facing too many stimuli at once, and anxiety, depression, somatic complaints and fatigue. The positive higher-order dimension, reflecting an increased sensitivity to subtle stimuli and to the emotional loading of diverse stimuli, showed a weak association with each of these outcomes (De Gucht et al., 2022).

Thus, the results of previous studies indicate that SPS, and more specifically the negative aspects of SPS, are related to clinical outcomes such as anxiety and depression. This implies a distinction between the impact of negative versus positive stimuli, which in turn goes in the direction of the underlying assumption of the Diathesis-Stress model.

A concept that plays a not unimportant role within the Diathesis-Stress framework is Resilience. Although there is no single, uniform, and universally accepted definition of Resilience, existing definitions include the element of healthy, adaptive, or positive functioning in the aftermath of adversity (Southwick et al., 2014). In line with this, Resilience is considered to be a (personality) factor that can protect individuals from the harmful effects of adversity (Pluess & Belsky, 2013).

Despite its association with the Diathesis-Stress framework, only a limited number of studies have looked at the relationship between Resilience and SPS. Gulla and Golonka (2021) found that a higher total score on the HSPS as well as on the factor Emotional Reactivity (a negative aspect of SPS) was related to less resilience, while the factor Sensing the Subtle (a positive aspect of SPS) was associated with more resilience. A recent study (Iimura, 2022) looked at both the relationship between SPS and resilience and the relationship between resilience and stress (in this case COVID-19 related stress). This study concluded that high SPS was related to low resilience and that low resilience was in turn related to higher stress levels. In addition, a significant indirect effect was found of SPS on COVID-19 related stress, via resilience.

Regarding the relationship between resilience and distress, the existing empirical literature is quite unambiguous. Both non-clinical (Harker et al., 2016; Anasori et al., 2019) and clinical samples (Min et al., 2013; Matzka et al., 2016) found a negative association between resilience and psychological distress. In addition, the study by Anasori et al. (2019) also showed that within a working population, resilience partially mediates the relationship between bullying and emotional exhaustion (as a sub-dimension of burnout). A systematic review among caregivers found that resilience was negatively correlated with emotional distress (Palacio et al., 2020). Another systematic review, based on 12 studies conducted in patients with chronic somatic conditions showed a negative association between resilience on the one hand, and anxiety, depression, and physical complaints on the other (Cal et al., 2015). Finally, a meta-analysis run across 55 studies, conducted in chronically ill samples, showed a negative association between resilience, anxiety, and depression (Färber & Rosendahl, 2018).

Summarizing, the existing literature shows that there is a pattern (albeit differentiated depending upon whether we look at the negative or positive dimension of SPS) of associations between SPS and psychological and somatic distress, supporting the Diathesis-Stress model. Secondly, there is a negative association between resilience and distress. It is however unclear whether and to what extent resilience mediates the association between SPS, and, more specifically, the positive versus negative dimension of SPS, and distress. Finally, each of these relationships have not been studied before, especially in interaction with each other, in a gifted sample.

Research questions

  1. A.

    To what extent does SPS measured at baseline (T0) predict (psychological and physical) distress at follow-up (T1), and is this effect different depending upon whether we look at the positive or negative dimension of SPS?

  2. B.

    To what extent does resilience at T0 mediate the effect of SPS on distress, and is there a differential pattern of mediation if we compare the positive to the negative dimension of SPS?

Methods

Procedure

The present study was a one-year follow-up study, conducted in collaboration with The Gifted Adults Foundation in The Netherlands (Instituut Hoogbegaafdheid Volwassenen, IHBV), an organization focused on improving the living environment of gifted adults.

The baseline data were collected between September 2019 and February 2020. Respondents were recruited by means of a call for participation on the website and in the newsletters of the IHBV and Mensa Netherlands. In addition, a call for participation was made through the network of Exentra, a Belgian Expertise Centre for Giftedness. Finally, posts were placed on Facebook and LinkedIn. After reading the information letter and signing the informed consent, participants could click a link to the online survey (Qualtrics).

A total of 1,575 responses were recorded and subjected to data cleaning, which involved removing duplicate entries, responses indicating informed dissent, incomplete responses to the Sensory Processing Sensitivity Questionnaire (SPSQ), or individuals reporting not to be highly gifted. This resulted in a dataset comprising 1,141 valid responses that were used for subsequent analysis. Among these respondents, 46.8% reported having taken an official IQ test administered by a qualified psychologist and achieving a score exceeding the threshold of 130. Additionally, 23.5% indicated that they had completed an online IQ test, obtaining scores within the gifted range. Furthermore, 31.1% stated that they were either current or former members of Mensa, implying that they had achieved scores in the top 2% on Mensa’s official admission test, typically corresponding to an IQ score of 132 or higher. In total, 71.3% of the respondents had either undergone official IQ testing, taken an online IQ test, or had affiliations with Mensa. The remaining participants were identified as gifted either through evaluation by Exentra or based on the criteria outlined in the Delphi model’s definition of giftedness (van Thiel et al., 2019).

The baseline measurement already announced the intention to survey respondents again one year later. With this in mind, baseline respondents were asked to enter their email address at the end of the questionnaire if they wanted to be contacted for the follow-up measurement. Shortly before sending emails to those respondents who had expressed an interest in participating in the follow-up measurement, announcements were placed in the digital newsletters of IHBV, Mensa and Exentra to alert respondents to the fact that they would receive an email soon containing a link to the questionnaire. The follow-up data were collected between November 2020 and January 2021. In total 738 respondents participated in the follow-up (response rate: 64.7%).

Measures

Sensory processing sensitivity (SPS), measured at T0

The Sensory Processing Sensitivity Questionnaire (SPSQ; De Gucht et al., 2022) was used to measure sensory processing sensitivity/high sensitivity. The SPSQ consists of a positive and negative higher-order dimension. The negative dimension consists of two subscales, Emotional and Physiological Reactivity (EPR; 11 items) and Sensory Discomfort (SD; 8 items), while the positive dimension consists of four subscales, Sensory Sensitivity to Subtle Internal and External Stimuli (SIES; 6 items), Sensory Comfort (SC; 5 items), Social-Affective Sensitivity (SAS; 8 items), and Aesthetic Sensitivity (AS; 5 items). The SPSQ comprises a total of 43 items. Each item is evaluated on a 7-point scale (1= ‘not at all’, 4= ‘moderately’, and 7= ‘extremely’). The total score for the negative dimension corresponds to the sum score of EPR and SD; the total score for the positive dimension to the sum score of SIES, SC, SAS, and AS. The SPSQ total score corresponds to the sum of the score on the positive and the negative higher-order dimension. For the current study, only the SPSQ total score and the two higher-order dimensions were included in the analyses. The internal consistency reliability for both the total SPSQ score (α = 0.93) and the negative and positive higher dimension were good (α = 0.90 and 0.92, respectively).

Depression, measured at T1

The Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001) was used to determine (the severity of) depressive symptoms. The PHQ-9 consists of 9 items, with each item corresponding to one of the DSM-V criteria for Major Depression. Respondents indicated how often, in the past two weeks, they had experienced each symptom. Each item is scored on a 4-point scale ranging from “not at all” to “almost every day.” The total severity score is calculated by adding up the scores on the 9 items. Internal consistency was good (α = 0.86).

Anxiety, measured at T1

The Generalized Anxiety Disorder-7 (GAD-7; Spitzer et al., 2006), was administered to assess generalized anxiety. The GAD-7 consists of 7 items where each item addresses a symptom of generalized anxiety. Respondents must indicate how much they have been bothered by each symptom in the past two weeks, using a 4-point scale ranging from “not at all” to “almost every day.” The total severity score is calculated by adding up the scores on the 7 items. The internal consistency was good (α = 0.87).

Physical complaints, measured at T1

The Patient Health Questionnaire-15 (PHQ-15; Kroenke et al., 2002), was used to address the presence and severity of 15 physical symptoms over the last four weeks. Each item is answered using a 3-point scale ranging from “not bothered at all” to “bothered a lot.” The total severity score is calculated by adding up the scores on the 15 items. Internal consistency reliability was acceptable (α = 0.79).

Fatigue, measured at T1

The Multidimensional Fatigue Inventory (MFI; Smets et al., 1995) was administered to assess fatigue severity. The MFI comprises 5 subscales and consists of 20 items in total. Each item is scored on a 5-point scale ranging from “Yes, that is true” to “No, that is not true”. The total score is calculated based on the sum of the scores on the 20 items. Only the General Fatigue subscale (4 items) was used in the current study. The internal consistency of this subscale was good (α = 0.88).

Resilience, measured at T0

The Resilience Evaluation Scale (RES; van der Meer et al., 2018) was used to assess resilience. The RES consists of two subscales, i.e., Self-efficacy and Self-confidence, and comprises a total of 9 items. Each of the items is scored on a 5-point scale with “completely disagree” at one end of the scale and “completely agree” at the other. For the purpose of the current study, only the total score on the RES was used; this score was calculated based on the sum of the scores on the 9 items. The internal consistency of this total score was good (α = 0.85).

Statistical analysis

A Structural Equation Modelling (SEM) framework was used to test the research questions. This modelling technique allows a mediation process to be extended to multiple outcomes and provides the option to handle missing data without listwise deletion (Gunzler et al., 2013). The models were estimated using maximum likelihood; standard errors were bootstrapped using 5000 samples. Full-Information Maximum Likelihood (FIML) was applied to handle missing data, as missing data could be assumed to be Missing Completely At Random (MCAR) according to Little’s (1988) MCAR test, χ²(14) = 4.26, p = .99.

For research question A, we additionally conducted an analysis predicting potential clinically significant levels of depression, anxiety, and physical complaints. In the instruction manual, the PHQ and GAD diagnostic modules set a cut-off score of 10 or higher as an indication of moderate, moderately severe, or severe symptom levels of depression, anxiety or physical complains (Kroenke & Spitzer, 2010). We used the classification (based on the above cut-off score) at follow-up as the dependent variable in a logistic regression model, with the SPSQ total score (or its positive and negative dimensions) measured at baseline as the independent variable(s). Prior to this analysis, we Z-transformed the scale scores to obtain standardized effects (Agresti, 2012). As measures of effect size, the odds ratio and the McFadden pseudo R-squared were used.

We followed the typology of mediation and non-mediation as described in Zhao et al. (2010), distinguishing complementary mediation, competitive mediation, indirect-only mediation, direct-only non-mediation and no-effect non-mediation. All models (SEM and logistic regression) included the covariates age and gender. No issues with collinearity were found, as the maximum value of the Variance Inflation Factors (VIFs) was 4.59. Values exceeding 5 or 10 are considered problematic (James et al., 2013).

Results

Descriptive statistics

The mean age of respondents was 44.68 (SD = 12.02); 63.4% (n = 468) was female. With respect to employment status, almost half of the participants in the sample were working full-time (n = 340, 46.1%), about one-third part-time (n = 222, 30.1%), 105 participants were unemployed (14.2%), 39 were students (5.3%) and 32 retired (4.4%). The majority of respondents completed higher education (n = 607, 82.2%), followed by secondary education (n = 97, 13.1%), secondary vocational education (n = 30, 4.1%) and primary education (n = 4, 0.5%).

In Table 1, descriptive statistics and bivariate correlations are shown for variables of interest. Moderate correlations were found between the clinical outcome variables and the total SPSQ (r = .23–0.35), as well as the negative dimension of the SPSQ (r = .27–0.36). Overall, the positive dimension of the SPSQ was weakly correlated to the clinical outcomes (r = .12–0.23). Negative associations were found between resilience and the SPSQ-Total (r = − .20) as well as the negative SPSQ dimension (r = − .43); the positive dimension was weakly positively related to resilience (r = .10). Moderate negative associations were found between resilience and the clinical outcomes (r = − .38 – − 0.29).

Table 1 Descriptives statistics and associations between variables of interest (N = 738)
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Effects of SPS on distress

The SPSQ total scale score at baseline had significant positive effects on depression (β = 0.24), anxiety (β = 0.31), physical complaints (β = 0.31) and fatigue (β = 0.23) one year later (all ps < 0.001). This indicates that, overall, respondents with a higher degree of SPS at baseline reported higher levels of depressive symptoms, anxiety, physical complaints, and fatigue at follow-up (see Fig. 1A). For the negative dimension of the SPSQ, positive effects were also found on depression (β = 0.26), anxiety (β = 0.33), physical complaints (β = 0.28) and fatigue (β = 0.27). In contrast to the negative dimension, no significant effects were found for the positive dimension of the SPSQ on psychological distress one year later. For physical distress, significantly higher levels of physical complaints were reported (β = 0.08, p = .028), but no effect of the positive dimension was found on fatigue (β = − 0.01, p = .77; see Fig. 1B).

Fig. 1
figure 1

Standardized path coefficients testing the effect of the total SPSQ score (Fig. 1A on the left) and the positive and negative dimensions of the SPSQ (Fig. 1B on the right) on psychological and physical distress at one year follow-up. Note Effects controlled for age and gender; * p < .05, ** p < .01, *** p < .001

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Logistic regression

In the sample, 23.4%, 21.3%, and 24.7% of participants exhibited indications of moderate to severe symptom levels of depression, anxiety, and physical complaints, respectively. In Table 2, logistic regression results predicting these indications are presented. The six models were statistically significant, with pseudo R-squared values ranging from 0.068 to 0.112.

Table 2 Coefficients, significance tests, and overall model statistics of logistic regression analysis predicting moderate to severe symptom levels of depression, anxiety, and physical complaints by scale scores of the sensory processing sensitivity questionnaire (SPSQ)
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In Model 1, 3 and 5, a significant positive effect of the SPSQ total score was found predicting a severity score of 10 or more for Depression (B = 0.58, SE = 0.11, p < .001), Anxiety (B = 0.77, SE = 0.12, p < .001) or Physical Complaints (B = 0.64, SE = 0.11, p < .001). An increase by one standard deviation of the total SPSQ score resulted in a higher estimated likelihood of reporting moderate to severe symptoms at follow up. The likelihood increased most for anxiety (OR = 2.17), followed by physical complaints (OR = 1.89) and depression (OR = 1.78), keeping the covariates gender and age constant. In Model 2, 4 and 6, we tested the effects of the SPSQ positive and negative dimensions on moderate to severe symptom scores. The effects of the negative dimension were significant for each clinical outcome (all ps < 0.001). Respondents were 2.43 times more likely (OR = 2.43) to report moderate to severe symptoms of anxiety at follow-up when their negative SPSQ scale scores were one standard deviation higher, while keeping the other variables constant. Symptoms of depression and physical complaints also had a higher likelihood to be clinically significant (OR = 1.71 and 1.76 respectively). The SPSQ positive dimension did not have any statistically significant effects predicting the group of respondents with the higher severity scores.

Mediating role of resilience

The mediating role of resilience for the relationship between the SPSQ total score and distress is illustrated in Fig. 2A. Positive direct effects were found of SPSQ-Total on the clinical outcomes one year later (all ps < 0.001). Concerning the indirect effects, the SPSQ was negatively related to resilience (β = − 0.22) at baseline, and resilience was negatively related to depression (β = − 0.31), anxiety (β = − 0.26), physical complaints (β = − 0.22) and fatigue (β = − 0.26) at follow-up. Resilience mediated the relationship between the SPSQ-Total and depression (β = 0.07), anxiety (β = 0.06), physical complaints (β = 0.05) and fatigue (β = 0.06; all ps < 0.001), respectively. As both direct and indirect effects on distress point in the same direction, the pattern of mediation is complementary.

Results of the SEM analysis testing resilience as a mediator for the positive and negative SPSQ dimensions are presented in Fig. 2B. Overall, resilience mediated the relationship between the positive and negative dimensions of the SPSQ on the one hand and the clinical outcomes on the other.

For the negative SPSQ dimension, positive indirect effects were found for depression (β = 0.19), anxiety (β = 0.14), physical complaints (β = 0.13) and fatigue (β = 0.14; all ps < 0.001). A higher score on the negative dimension was associated with a lower resilience (β = − 0.57, p < .001), leading to increased levels of psychological and physical distress. Additionally, the negative dimension of the SPSQ also directly increased levels of anxiety, physical complaints, and fatigue. For depression no significant direct effect was found (β = 0.07, p = .11). Thus, for anxiety, physical complaints and fatigue, complementary mediation was found, and for depression indirect-only mediation.

For the positive SPSQ dimension, indirect effects were significant and negative for depression (β = − 0.11), anxiety (β = − 0.08), physical complaints (β = − 0.07) and fatigue (β = − 0.08). For depression, anxiety and physical complaints, the sign of the indirect effects points in the opposite direction of the direct effects and the pattern of mediation is competitive. A higher score on the positive dimension of the SPSQ leads to more resilience (β = 0.33, p < .001), which, in turn, leads to (relatively) less distress. For fatigue, indirect-only mediation was found, as the direct effect of the positive SPS dimension was not significant when the indirect effect via resilience was taken into account, β = 0.07, p = .10.

Fig. 2
figure 2

Visual representation of the direct and indirect effects of the total SPSQ-score (A) and its positive and negative dimensions (B) on psychological and physical distress at one year follow-up, with resilience as a mediator. Note Standardized coefficients of the indirect effects (indicated by the dashed lines) in between brackets, and in diagram B for the negative and positive SPSQ-scores respectively; effects controlled for age and gender; * p < .05, ** p < .01, *** p < .001

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Discussion

A high degree of SPS directly predicts all clinical outcomes, serving as a determinant of (i) the extent to which respondents experience emotional or physical distress and (ii) an increased risk of experiencing moderate to severe anxiety, depressive symptoms, or physical complaints. This finding is consistent with previous studies conducted in student populations or the general population using the HSPS (Grimen & Diseth, 2016; Dinc et al., 2021; Bakker & Moulding, 2012; Yano et al., 2019; Benham, 2006). However, the difference between this study and previous ones is that this study was conducted in a gifted sample, using a more exhaustive questionnaire to measure SPS. Additionally, it also considers clinically significant cut-offs for the different outcomes and employs a prospective design rather than a cross-sectional one. With respect to the higher-order dimensions of SPS, the negative dimension has medium to strong effects, whereas the positive dimension has little to no effect on clinical outcomes. Although prior research did not distinguish between a negative and positive higher-order dimension of SPS, the current results are in line with earlier studies that found associations between the HSPS subscales Ease of Excitation and Low Sensory Threshold, both more negative aspects of SPS, and distress (Liss et al., 2008; Grimen & Diseth, 2016; Takahashi et al., 2020; Bordary et al., 2021). With respect to the positive higher-order dimension, a comparison with previous studies is challenging since results regarding the relationship between distress and the HSPS subscale Aesthetic Sensitivity, measuring a positive aspect of SPS, are inconsistent (Liss et al., 2008; Bordary et al., 2021; Grimen & Diseth, 2016).

Regarding potential indirect effects, the present findings indicate that high SPS is associated with less resilience, which in turn predicts a higher score on depression, anxiety, fatigue, and physical complaints. Notwithstanding this indirect effect, the direct effect of SPS on each of the clinical variables remains.

The relationship that was demonstrated between high SPS and resilience is consistent with the findings of previous studies carried out in other non-gifted populations (Gulla & Golonka, 2021; Iimura, 2022). The same is true for the relationship between resilience and distress (Min et al., 2013; Cal et al., 2015; Harker et al., 2016; Matzka et al., 2016; Anasori et al., 2020). The mediation effect found in the current study is in line with the findings of Iimura (2022). The Iimura study however solely focusses on the indirect impact of SPS (via resilience) on COVID-19 related stress, whereas the current study includes a wide range of psychological and physical distress outcomes. In addition, the present study not only looks at general sensitivity, but also discriminates between the negative and positive dimension of SPS.

The mediation effect of the negative and positive higher-order dimension of SPS (via resilience) points in opposite directions. For the negative dimension, the mediation effect is complementary, implying that less resilience reinforces the (direct) negative impact of SPS on clinical variables. In contrast, for positive SPS, the mediation effect is competitive, meaning that resilience buffers the negative (direct) effect of SPS on clinical outcomes. Overall, the indirect effect is strongest for the negative higher-order dimension. The latter finding can be seen as a confirmation that resilience does indeed protect the highly sensitive individual against the adverse effects of negative experiences or environmental stimuli, as hypothesized within the Diathesis-Stress model (Pluess & Belsky, 2013).

Strengths and limitations of the study

One of the main strengths of this study is its large sample size, combined with the fact that there are two measurement points. This has enabled us to examine whether general sensory sensitivity on the one hand, and the positive and negative dimensions of sensitivity on the other hand, measured at baseline, predict several clinical outcomes one year later. Including resilience in the study has permitted to test, albeit indirectly, one of the assumptions underlying the Diathesis-Stress model, namely whether resilience is a protective factor safeguarding highly sensitive individuals against the negative impact of SPS on distress. An additional strength of this study is that, unlike previous studies, we administered a questionnaire that includes a wide range of both positive and negative aspects of SPS.

The primary limitations of this study are associated with the use of online self-report questionnaires to address the research questions. One limitation of this approach is the risk of selection bias. Although we took measures to recruit potential respondents through various avenues and channels, there remains the possibility that individuals with a more pronounced interest in the research topic may be more inclined to respond to the call for participation in the study. A second risk associated with an online survey is that some respondents may initiate the questionnaire but not complete it, or they may go through it too quickly, compromising the reliability of their provided responses. We attempted to mitigate this risk by excluding respondents who completed the questionnaire too rapidly from the analyses. Respondents who incompletely filled out the questionnaire were also excluded. The exclusion of certain respondents, on the one hand, enhances the reliability and validity of the study, but, on the other hand, may introduce non-response bias, as it is possible that the responses of excluded individuals, for various reasons, may differ from those of the subjects included in the analyses. A potential additional limitation of our study lies in the method used to compose our sample. Although the study was conducted within a specific population, namely highly gifted individuals, convenience sampling was employed. A call for participation in the study was distributed through various organizations and channels. While this method is the most straightforward when employing questionnaire-based research, for future research, setting quotas for specific demographic groups could be considered to enhance the representativeness and diversity of the sample. Particularly within the scope of this study, efforts should be made to achieve a better gender balance.

Suggestions for future research

One of the goals of the current study was to test (a specific aspect of) the Diathesis-Stress model in a gifted sample. Follow-up research could tie into this by zooming in more on the second part of the Differential Susceptibility framework, namely Vantage Sensitivity. A possible avenue here is to conduct research with people who have recently experienced something they considered to be (particularly) positive, for example an important promotion. One could then investigate more specifically (1) whether the impact of such a positive event is stronger with people who score higher on SPS, and (2) whether this effect is more pronounced with those respondents who score higher on the positive than on the negative dimension of SPS.

Secondly, since resilience is an individual characteristic that is called upon when confronted with a negative experience, future research could focus on the extent to which resilience may buffer between high sensitivity and distress within a sample of respondents who are confronted with something (particularly) negative. In this context, one might consider, for example, individuals who have recently experienced a traumatic event or individuals who have experienced chronic stress for some time (e.g., work stress or financial stress). If it is indeed true that resilience can provide a buffer between SPS and distress in stressful conditions, follow-up research could focus on the development and evaluation of psychological interventions aimed at increasing resilience.