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. 2022 Feb;19(1):20–28. doi: 10.36131/cnfioritieditore20220104

School Refusal Behavior: Role of Personality Styles, Social Functioning, and Psychiatric Symptoms in a Sample of Adolescent Help-Seekers

Renato Carpentieri 1,2,✉, Maria Elena Iannoni 3, Martina Curto 4, Mario Biagiarelli 5, Giulia Listanti 1, Maria Paola Andraos 2, Beatrice Mantovani 6, Carla Farulla 1, Simona Pelaccia 1, Giuseppe Grosso 6, Anna Maria Speranza 2,†, Cinzia Sarlatto 1,†
PMCID: PMC8951164  PMID: 35360464

Abstract

Objective

School refusal (SR) in adolescence represents an important risk factor associated with adverse consequences. Although many clinical features of adolescents presenting with SR have been studied, the relationship between SR and personality styles—specifically in the help-seeking population—remains unclear. The present study aimed at investigating differences in personality style, adaptive functioning, and symptomology between Italian help-seeking adolescents who refused (SRa) and did not refuse (non-SRa) to attend school, to provide preliminary evidence of personality patterns in adolescent help-seekers presenting with SR.

Method

The study sample was comprised of 103 help-seeking adolescents (54 female, 49 male) aged 14–18 years. Participants were recruited during their first clinical visit and evaluated using the Shedler and Westen Assessment Procedure – Adolescent version (SWAP-A), the Hamilton Rating Scale for Depression (HAM-D), the Hamilton Rating Scale for Anxiety (HAM-A), the Maniac Rating Scale (MRS), the Global Assessment of Functioning (GAF), the Global Functioning Social Scale (GFSS), and the Global Functioning Role Scale (GFRS). Differences in the studied variables between SRa and non-SRa were measured and a multivariable logistic regression analysis was performed to identify possible predictive factors of SR.

Results

SRa presented with more anxious and depressive symptomatology and worse social functioning compared to non-SRa. With respect to personality, SRa displayed more schizoid and schizotypal characteristics and fewer adaptive and healthy personality features. Irrespective of any differences between groups, SRa were largely characterized by inhibited–self-constricted and emotionally dysregulated personality styles.

Conclusions

The results suggest that personality styles are clinical features that may contribute to broadening our knowledge of SR behavior and aid in the detection of SRa, also in the help-seeking population. The findings have clinical, social, and political implications for prevention, diagnosis, and treatment, in both clinical and non-clinical settings. However, more data are needed on personality features to clarify their contribution to the more complex phenomenon of school absenteeism.

Keywords: school refusal behavior, personality style, social functioning, adolescence, SWAP-A

Introduction

The phenomenon of school non-attendance represents a significant issue for educators and mental health professionals. Research has clearly indicated that problematic absenteeism is a major risk factor for both short-term (e.g., suicide attempts; affective disorders; violence; consumption of alcohol, marijuana, or other substances) and long-term consequences (e.g., school dropout, poverty, delinquency, psychiatric disorders in adulthood) (Kearney, 2008; Esch et al., 2014). The scientific literature distinguishes between different patterns of problematic school non-attendance. In line with Heyne and Sauter (2013), the present study focused on the phenomenon of school refusal (SR). SR consists of reluctance to attend school and/or total rejection of school, often leading to prolonged absenteeism. Typically, the student’s parents know that their child is staying home during school hours, despite their attempts to secure their child’s attendance. When forced to attend school, the student will often experience emotional distress; nevertheless, antisocial behavior is not necessarily present. Importantly, SR should be differentiated from truancy, which is characterized by illegal and unexcused absenteeism from school without parental awareness, with the student experiencing actual emotional distress when attempting to attend school (Kearney, 2003).

SR occurs in approximately 1–2% of the general youth population and 5–16% of the youth psychiatric population. Moreover, secondary school students report SR more frequently than primary school students (Maynard et al., 2015). According to the “Education and Training Monitoring” project (Commissione europea, 2014), all European countries committed to reducing the rate of early school dropout to below 10% by 2020. SR research is fundamental to continue progress in this direction and counteract the phenomenon, which has significant social, economic, and mental health implications.

Although neither the Diagnostic and Statistical Manual of Mental Disorders – 5th edition (DSM-5; APA, 2013) nor the International Classification of Mental and Behavioral Disorders (ICD-10; WHO, 2007) classifies SR as a psychiatric disorder, young people who refuse school frequently meet the criteria for at least one psychiatric disorder. Indeed, research in the United States has estimated that 46% of all school dropouts are caused by a psychiatric disorder (Vander Stoep et al., 2003). Thus, SR appears to be strongly associated with—but not synonymous with—psychopathology (Maynard et al., 2015). At present, many clinical conditions have been identified as contributing factors to SR during adolescence—especially anxiety, mood, and somatic disorders (Askeland et al., 2020; Gonzálvez et al., 2018; Matsuura et al., 2020; Maynard et al., 2015; Melkevik et al., 2016; Seçer & Ulaş, 2020).

According to more recent research, social functioning seems to also play a key role in SR. Students who refuse school due to negative affectivity and anxious symptoms tend to show more loneliness and deficiencies in adaptive social functioning and the establishment of social relationships, compared to adolescents who either do not refuse school or refuse to attend school for tangible reinforcements (Gonzálvez et al., 2019; Ingul & Nordahl, 2013; Ingul et al., 2019). This suggests that good social relationships with peers or classmates can prevent SR behavior (Gonzálvez et al., 2019; Havik et al., 2015). However, we still know little about this phenomenon in clinical settings.

Only few studies have attempted to detect the personality features that are significantly associated with SR. Some research has shown that adolescents reporting withdrawal behaviors, introverted character tendencies, low sociability, and low self-esteem demonstrate worse school attendance (Berg & McGuire, 1971; Corville-Smith et al., 1998; Okuyama et al., 1999). Furthermore, the results of Janosz et al. (1997) suggest that repression and neuroticism may also be related to school dropout. High repression is characterized by the unconscious suppression of negative affect and difficulty recognizing one’s own emotions; high neuroticism reflects a general tendency toward emotional instability and feelings of victimization; and high scores on both of these personality dimensions relate to a higher probability of school dropout in adolescent students.

Ingul & Nordahl (2013) suggested that simply treating the anxiety problems of anxious school-refusing adolescents may not be sufficient, as these students also show higher negative personality traits, social anxiety, panic symptoms, and behavioral problems compared to non–school-refusing adolescents. Another study (Lounsbury et al., 2004) investigated personality traits in young students (aged 10–18 years) according to the “Big Five” trait model and other selected personality dimensions. The results showed that openness, conscientiousness, and emotional stability were negatively related to school absences in all grade levels investigated, while agreeableness was positively related to school absences among teenage students. A more recent study confirmed that emotional stability plays a key role in this phenomenon (Havik et al., 2015).

The actual relationship between SR and personality traits remains unclear, also in the clinical population. Further research in this area could have useful implications for treatment and prevention, and it could also clarify the fragmentary nature of personality from a more clinical approach.

As previously discussed, psychopathology and social functioning frequently co-occur in SR. Furthermore, personality features seem to play a specific role in this phenomenon. Considering these preconditions, the present study investigated and clarified differences in personality style, adaptive functioning, and symptomology between Italian help-seeking adolescents presenting with SR (i.e., SRa) and help-seeking adolescents who were not presenting with SR (i.e., non-SRa), using clinician-report tools. The hypotheses were as follows:

  1. help-seeking SRa would present more psychiatric symptoms relative to help-seeking non-SRa;

  2. help-seeking SRa would present more social difficulties relative to help-seeking non-SRa;

  3. help-seeking SRa would present greater impairment in adaptive personality functioning indexes relative to help-seeking non-SRa; and

  4. Schizoid, schizotypal, avoidant, and borderline personality traits would be higher in SRa relative to non-SRa.

Materials and Methods

Participants

The study sample of help-seeking youths was recruited from the set of outpatients who had their first psychiatric consult at the Anxiety and Mood Disorders in Adolescence Clinic (“Sant’Andrea Hospital,” Psychiatry Unit) between 2018 and 2019. A total of 103 consecutive help-seeking adolescents (49 male, 47.57%; 54 female, 52.43%), aged 14–18 years (M = 16.2; SD = 1.14), of whom 28 (27.14%) were SRa, participated in the study. All participants were Caucasian Italian citizens. Participants (or their legal guardians) provided competent, written, voluntary, informed consent or assent to participate in the study. They also agreed to the anonymous and aggregate analysis of their data and the reporting of the clinical findings. They were assured that study refusal would have no effect on their treatment. All participants assented to take part in the study. Exclusion criteria were: intellectual disability; the presence of a severe medical condition; and truancy (school absenteeism), as defined by Heyne and Sauter (2013). No subjects were excluded from the study, as all participants met the inclusion criteria.

Instruments

Hamilton Rating Scale for Anxiety

The Hamilton Rating Scale for Anxiety (HAM-A; HAM-A; Hamilton, 1959) was one of the first rating scales to be developed to measure the severity of anxiety symptoms, and it is still widely used in both clinical and research settings. The scale consists of 14 items defined by a series of symptoms, measuring both psychic and somatic anxiety. Items are scored on a 5-point scale ranging from 0–4 (e.g., “Anxious mood: worries, anticipation of the worst, fearful anticipation; irritability: not present”). The reliability index (Cronbach’s alpha) is 0.91.

Hamilton Rating Scale for Depression

The Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960) is a clinical, semi-structured rating scale that measures the intensity and severity of depression. The original version contains 17 items that detect symptoms of depression experienced over the prior week. Eight items are scored on a 5-point scale ranging from 0–4 or 0–2 (e.g., “Depressed mood, gloomy attitude, pessimism about the future, feeling of sadness, tendency to weep: extreme symptoms”). The reliability index (Cronbach’s alpha) is 0.87.

Young Mania Rating Scale

The Young Mania Rating Scale (YMRS; Young et al., 1978) is the most widely used tool for assessing the intensity of manic symptoms. The scale consists of 11 items, with symptom severity scored from 0–8 or 0–4 (e.g., “Increased motor activity/energy: excessive energy, hyperactive at times, restless but can be calmed”). The reliability index (Cronbach’s alpha) is 0.79.

Global Assessment of Functioning

The Global Assessment of Functioning (GAF; APA, 2001) is internationally well-known and widely used for scoring the severity of psychiatric illness. The index is based on a 100-point scale divided into 10-point intervals, each with anchor points describing relevant symptoms and levels of functioning (e.g., “Serious symptoms or any serious impairment in social, occupational, or school functioning”).

Global Functioning Social Scale

The Global Functioning Social Scale (GFSS; Auther et al., 2006) is a derivative of the GAF. Scores range from 1–10, with 1 representing extreme social dysfunction. The scale includes detailed anchor points for each rating interval, to increase reliability (e.g., “Serious impairment independently. Failing multiple courses in mainstream school, may lose job, or unable to complete most homemaking tasks independently”).

Global Functioning: Role Scale

Scores on the Global Functioning Role Scale (GFRS; Niendam et al., 2006) range from 1–10, with 1 representing extreme role dysfunction. The scale includes detailed anchor points for each rating interval, to increase reliability (e.g., “Alone and socially isolated. Rarely leaves home. Rarely answers the phone or the door. Rarely participates in interactions with others at home or in other settings—e.g., work, school”).

Shedler- Westen Assessment Procedure for Adolescents

The Shedler-Westen Assessment Procedure for Adolescents (SWAP-200-A; Westen et al., 2005) consists of 200 personality-descriptive items. Each item describes the personality characteristics of an adolescent patient as “very well, somewhat, or not at all.” The tool is based on a Q-sort fixed distribution, which requires clinicians to assign each statement into one of eight “piles.” Items in pile 7 are considered “most reflective statements of personality” and items in pile 0 are considered “not reflective statements of the patient’s personality.” The distribution follows a fixed number of assignable statements, growing from 0–7. The presence of one or more personality disorders is determined when the patient’s personality indexes are ≥ 60T and the adaptive functioning scale is < 60T. Scores ranging from 55T–60T are indicative of subclinical traits of personality disorders or styles. The reliability index (Cronbach’s alpha) is > 0.80, with a median of 0.86 (Shedler et al., 2014; Westen et al., 2005).

Procedure

During the first clinical medical consult, we informed patients about the study aims and provided the adolescents or their legal guardians with informed consent forms. An outpatient manager collected anamnestic data from the adolescents and their parents. All patients assented to participate in the study.

Initially, we conducted a brief interview to assess particular issues. Specifically, previous failures at school (i.e., missing school requirements) were assessed using the categories of absence or presence; parents’ education was assessed in terms of years (i.e., 5, 8, 13, 18, > 18 years); annual financial income was assessed with respect to the family’s total annual financial income (i.e., < 24,000€, 24,000–36,000€, > 36,000€); and intellectual disability and severe and/or chronic medical conditions were assessed according to previous diagnoses from medical and psychiatric centers. Finally, SR behavior was assessed using five questions, administered to both participants and parents, in line with Heyne and Sauter’s framework (2013):

  1. “Is the adolescent reluctant or does he/ she refuse to attend school, and does this behavior often lead to prolonged absences of up to 30 days?” (Yes)

  2. “Does the adolescent stay at home during school hours and are the parents aware of this?” (Yes)

  3. “Are the parents trying to secure the adolescent’s attendance at school?” (Yes)

  4. “Does the adolescent experience emotional distress (e.g., anxiety, unhappiness) at the prospect of attending school?” (Yes)

  5. “Does the adolescent present any severe antisocial behaviors?” (No).

Affirmative answers to questions (a), (b), (c), and (d) and a negative answer to question (e) determined inclusion in the SRa group. Adolescents who did not meet these criteria (Heyne & Sauter, 2013) were included in the non-SRa group.

At the second consult (without parents), a trained psychiatrist with 4 years of clinical experience administered the HAM-A, HAM-D, YMRS, GAF, GFSS, and GFRS to assess the presence of anxious, depressive, or maniacal status on the basis of referred and observed symptoms. Functioning scales were used to assess the level of global, social, and role functioning.

At the third consult, one of the six clinical psychologists administered the SWAP-A. All of the clinical psychologists were aged between 27–33 years, and each had at least 4 years of clinical experience, with different theoretical orientations (i.e., three psychodynamic theory, one cognitive-behavioral theory, one systemic-family therapy, one transactional analysis theory). All had received training for the instrument. The measures was administered according to the clinical diagnostic interview (CDI), which has been judged adequate for research assessment. Furthermore, the administration and scoring of the protocol followed the recommendations of the original authors (Shedler et al., 2014).

At the second and third consults, the clinical scales were administered by separated clinicians, who were blind to the others’ scores and the research purpose. Inter-rater reliability was assessed using intra-class correlation coefficients and found to be ≥ 0.88 for the psychiatric scales at the second consult.

The HAM-A, HAM-D, YMRS, GAF, GFSS, and GFRS were administered 7–14 days after the patients’ first consult. The SWAP-A was administered 7–14 days after the administration of the aforementioned clinical scales. Subjects were diagnosed according to DSM-5 (APA, 2013) criteria and grouped into five diagnostic clusters: (1) absence of psychiatric disorder, (2) anxious spectrum, (3) unipolar depressive spectrum, (4) bipolar spectrum, and (5) a mixed cluster of diagnoses (i.e., psychotic spectrum disorder, obsessive-compulsive disorder, ADHD, somatoform disorder, eating disorder).

Statistical Data Analyses

Distributions of the socio-demographic variables were described for each group and tested using Pearson’s chi-squared test or the Z-test, as appropriate (table 1). Differences between SRa and non-SRa, in terms of psychiatric symptoms, social difficulties, adaptive functioning indexes, and personality traits (as assessed by the SWAP-A) were measured using the Z-test (table 2). Finally, a multivariable logistic regression was performed to test associations between GFSS, PD Schizotypal, PD Schizoid, HAM-A (tot), HAM-D (tot), and SR (table 3).

Table 1.

Socio-demographic characteristics of the two groups

School refusers (28) Non–school refusers (75) χ/Z p
Age 16.1 ± 1.13 16.3 ± 1.15 0.464
Sex (n, %) 0.20 1.000
Female 15 (27.8%) 39 (72.2%)
Male 13 (26.5%) 36 (73.5%)
Diagnosis (n, %tot) 12.57 0.183
None 0 9 (12)
Anxiety 5 (17.9) 15 (20)
Depressive 19 (67.9) 36 (48)
Bipolar 4 (14.2) 3 (4)
Mixed (psychosis, OCD, ED, ADHD, somatization) 0 12 (16)
Failure at school (n, %) 11 (39.2%) 18 (24%) 3.751 0.153
Father’s education (years) 13.6 ± 3.2 12.8 ± 3.04 (-1.12) 0.263
Mother’s education (years) 12.6 ± 3.58 12.4 ± 3.34 (-0.216) 0.829
Annual financial income 1.264 0.532
Low 4 (14.3%) 9 (12%)
Medium 15 (53.6%) 49 (65.3%)
High 9 (32.1%) 17 (22.7%)
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Table 2.

Differences between groups in psychiatric symptoms, social difficulties, adaptive functioning indexes, and personality traits, as measured by the SWAP-A

School refusers (28) Non–school refusers (75) Z -test p
HAM-A (tot) 21.39 ± 8.608 17.37 ± 9.818 (-2.1) 0.036
HAM-D (tot) 18.25 ± 6.937 14.53 ± 8.433 (-2.15) 0.031
GAF 51.39 ± 13.505 62.64 ± 13.294 (-3.50) < 0.001
GAF_past year 62.43 ± 15.029 69.37 ± 12.230 (-2.24) 0.025
GFSS 5.39 ± 1.370 6.27 ± 1.143 (-3.01) 0.003
GFRS 5.29 ± 1.410 6.24 ± 1.354 (-3.05) 0.002
MRS (tot) 4.21 ± 3.614 5.24 ± 6.067 (-0.53) 0.958
PD Paranoid 46.67 ± 5.712 45.90 ± 6.384 (-0.77) 0.441
PD Schizoid 47.53 ± 7.180 44.47 ± 6.371 (-2.00) 0.046
PD Schizotypal 48. 34 ± 5.719 45.88 ± 7.677 (-2.12) 0.034
PD Antisocial 48.04 ± 4.472 46.58 ± 6.503 (-1.89) 0.069
PD Borderline 48.91 ± 7.737 46.414 ± 8.202 (-1.61) 0.107
PD Histrionic 51.65 ± 7.226 49.56 ± 7.168 (-1.47) 0.141
PD Narcissistic 50.89 ± 8.114 48.84 ± 7.276 (-1.13) 0.258
PD Avoidant 48.33 ± 6.997 47.28 ± 7.380 (-0.61) 0.543
PD Dependant 48.62 ± 8.285 48.88 ± 7.234 (-0.16) 0.873
PD Obsessive-Compulsive 42.97 ± 5.073 42.94 ± 4.772 (-0.18) 0.859
PD Health Functioning 50.74 ± 7.817 56.73 ± 7.670 (-3.25) 0.001
Q Health Index 40.45 ± 7.855 45.62 ± 8.056 (-3.15) 0.002
Q Antisocial-Psychopathic 49.33 ± 5.662 48.05 ± 7.469 (-1.19) 0.236
Q Emotionally Dysregulated 58.42 ± 6.657 55.15 ± 7.877 (-1.70) 0.900
Q Avoidant-constricted 52.27 ± 9.884 49.22 ± 9.109 (-1.53) 0.126
Q Narcissistic 51.70 ± 7.990 49.27 ± 7.083 (-1.57) 0.116
Q Histrionic 50.23 ± 8.078 49.42 ± 7.026 (-0.43) 0.665
Q Inhibited self-critical 59.54 ± 12.532 60.28 ± 14.22 (-0.35) 0.730
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Note. GAF: Global Assessment of Functioning, current score; GAF_past year: Global Assessment of Functioning, past year score; GFRS: Global Functioning Role Scale; GFSS: Global Functioning Social Scale; HAM-A (tot): Hamilton Rating Scale for Anxiety, total score; HAM-D (tot): Hamilton Rating Scale for Depression, total score; MRS (tot): Maniac Rating Scale, total score; PD: Personality disorder according to the DSM-IV, assessed using the SWAP-A scale; Q: Q-Factor scores according to empirical features of personality, assessed using the SWAP-A scale.

Table 3.

Multivariable logistic regression model for factors associated with SR in a sample of 103 help-seeking adolescents

Factors B Odds ratio 95% confidence intervals value
GFSS -0.31 1.58 1.00-2.50 0.05
PD Schizoid 0.46 0.95 0.87-1.04 0.27
PD Schizotypal -0.05 1.03 0.94-1.12 0.51
HAM-A (tot) 0.03 0.98 0.90-1.07 0.71
HAM-D (tot) -0.16 0.99 0.89-1.1 0.85
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Note. GFSS: Global Functioning Social Scale; PD: Personality disorder according to the DSM-IV, assessed using the SWAP-A scale; HAM-A (tot): Hamilton Rating Scale for Anxiety, total score; HAM-D (tot): Hamilton Rating Scale for Depression, total score.

Analyses were conducted using the statistical software package SPSS version 20 (IBM SPSS 2009 for Windows, Rel. 20.0.0), with data spreadsheets based on Excel and a 2-tailed significance level set to p < 0.05.

Results

Response Rates

Table 1 presents the demographic and clinical characteristics of the sample. Subjects were divided into two groups based on the presence or absence of SR behavior, as discussed in the “Procedure” section. In the SRa group (N = 28), the average age was 16.1 ± 1.13; 53.6% were female; 39.2% had experienced at least one school failure; and 67.9% had a depressive spectrum disorder, 17.9% presented with an anxiety spectrum disorder, and 14.2% had a bipolar spectrum disorder. Fathers’ and mothers’ years of education amounted to, respectively, 13.6 ± 3.2 and 12.6 ± 3.58. In total, 14.29% of these families had a low annual financial income, 53.57% had a medium annual financial income, and 32.14% had a high annual financial income.

In the non-SRa group (N = 75) the average age was 16.13 ± 1.15; 52% were female; 24% had experienced at least one school failure; and 48% had a depressive spectrum disorder, 20% presented with an anxiety spectrum disorder, 4% had a bipolar spectrum disorder, 16% had a different spectrum diagnosis (i.e., psychotic spectrum disorder, obsessive-compulsive disorder, eating disorder, ADHD, somatoform disorder), and 12% had no psychiatric disorder. Fathers’ and mothers’ years of education amounted to, respectively, 12.8 ± 3.04 and 12.4 ± 3.34. Concerning annual financial income, 12% of these families had low annual financial income, 65.33% had medium annual financial income, and 22.67% had high annual financial income.

There were no significant differences between groups in terms of age, gender, or diagnosis. Furthermore, no significant differences were found between groups with respect to previous school failures, parents’ education, or annual financial income (table 1).

An independent sample Z-test yielded a significant difference between groups on the dependent variables of interest. Specifically, SRa reported significantly more anxious and depressive symptoms (HAM-A [tot] p = .036; HAM-D [tot] p = .031) than non-SRa, as well as a lower level of global adaptive functioning (at both the point of assessment and 1 year prior to the assessment; GAF p < .001, GAF_past year p = .025) and lower levels of social and role functioning (GFSS p = .003, GFRS p = .002) (table 2). With respect to personality features, no significant differences emerged, except for PD Schizoid (p = .046) and Schizotypal (p = .034), which were higher in SRa; moreover, Health Functioning (p = .001) and the Q Health Index (p = .002) were lower in SRa.

A multivariable logistic regression analysis was conducted to examine the unique relationships between SR (measured as a dichotomous variable) and the variables found to significantly differ between SRa and non-SRa: GFSS, PD Schizoid, PD Schizotypal, HAM-A (tot), and HAM-D (tot). GAF, GAF_past year, and GFRS were excluded from these models, since they are too closely related to scholastic functioning, as reported in the “Instruments” section. Moreover, the personality indices of health functioning (i.e., PD, Q Health Index) include items that are opposed to SR (e.g., “Enjoys challenges; takes pleasure in accomplishing things”; “Is able to use his/her talents, abilities, and energy effectively and productively”), and therefore ensure a low score in global functioning. Since SRa did not attend school, we deemed it tautological to introduce these indices into the regression model.

The results of the multivariable logistic regression model showed a significative effect only for GFSS (OR = 1.58, 95% CI [1.00, 2.50], p = .05). Furthermore, although the previous comparisons between means were significant, no significative effects emerged for the other variables (i.e., PD Schizoid, PD Schizotypal, HAM-A, HAM-D).

Discussion

SR is a complex phenomenon with adverse short-and long-term implications. The present study aimed at: (a) investigating differences (in psychiatric symptoms, social functioning, and personality features) between SRa and non-SRa in the help-seeking population; and (b) investigating the psychiatric symptoms, social functioning, and personality features of SRa using clinician-report tools. Only few studies have collected data from clinical settings, despite the fact that SR has been reported to occur in 5–16% of all youths in these settings (Havik et al., 2015); accordingly, our analysis aimed at filling this gap in the literature by highlighting potential differences between SRa and non-SRa in a clinical setting. As hypothesized, SRa presented with more psychiatric symptoms, greater social impairment, and fewer adaptive personality features. With respect to personality features, more specifically, only some of the findings were in line with the hypotheses. These results are discussed below.

SR has been shown to be closely linked with anxious and depressive symptomatology. Accordingly, the present study found such symptoms to be more severe in SRa than non-SRa. These results are in line with previous studies and confirm the central importance of negative affect, which seems to be more severe in SRa, even in clinical settings. Of note, no significant differences between SRa and non-SRa emerged with respect to manic symptoms. Although few studies have investigated manic symptoms in this context, the present result is in line with previous evidence showing that approximately 85% of youths with severe SR have some negative psychiatric symptomatology, mainly concerning depressive syndromes and anxiety problems (Egger et al., 2003; Nayak et al., 2018). On the one hand, we would not expect more severe symptomology in SRa compared to non-SRa, precisely because SRa typically present with a depressed mood, low energy, and social withdrawal, which do not fit with manic symptoms. On the other hand, the lack of a significant difference found in the present study may have been due to the limited sample size, which included only a few non-SRa with bipolar spectrum disorder. Additionally, SRa showed worse adaptive and social functioning. These results support previous research showing that SRa have lower emotional stability and difficulties with interpersonal relationships (Ingul & Nordahl, 2013; Ingul et al., 2019; Melkevik et al., 2016; Vander Stoep et al., 2003). Moreover, the present results, involving a sample of help-seeking adolescents, agree with previous research findings involving non–help-seeking samples. This might support the hypothesis that SR has specific dysfunctional features, irrespective of the considered population. Ingul and Nordahl (2013) described severe SR in a non-clinical sample as a complex pattern composed of anxious and depressive symptoms and interpersonal problems. Conceivably, this pattern may be very similar in clinical contexts.

In accordance with the scientific literature, social functioning seems to be the distinguishing feature of SR; in particular, impaired social functioning has been shown to significantly increase the likelihood of SR. Indeed, feelings of loneliness, social isolation, and relational problems—especially among peers—should not be underestimated as a risk factor for SR (Scholte & Van Aken, 2006). Feelings of enjoyment and belongingness within a social group represent a fundamental developmental goal of adolescence; failure to achieve this goal may produce negative outcomes in teenagers, including SR.

A second important goal of the present work was to clarify the contribution of specific personality traits to SR. In this respect, SRa were found to present with more schizoid and schizotypal characteristics, but not as a specific personality trait, compared to non-SRa. Previous research has highlighted the association between SR and low agreeableness and introversion (Lounsbury et al., 2004), which, in part, describe interpersonal deficits and a relative lack of pleasure in social contexts.

Surprisingly, SRa did not show more avoidant characteristics than non-SRa. Despite this lack of difference, SRa were likely to present with avoidant-constricted personality traits. In the clinical population, psychiatric disorders might be associated with unspecified situational avoidance, linked to previous experiences of suffering. Indeed, this might represent the relational difficulties that characterize some SRa (Gonzálvez et al., 2018). On the one hand, these youths may show reduced interest in or avoidance of social exchange; on the other hand, they may be wary, scared, and worried about being overwhelmed by social life.

Another result of the present study that disagreed with the scientific literature was the finding that borderline personality features were not specific to SRa, but also found in non-SRa. Previous research has linked SR to deficits in emotional regulation and negative feelings (Lounsbury et al., 2004; Melkevik et al., 2015; Vander Stoep et al., 2003). The different findings in the present study may be explained by the fact that emotional dysregulation is not a specific condition of SR in a clinical sample. On the other hand, it seems that problematic emotional patterns characterized at least a sub-group of SRa. Indeed, for SRa, the school setting could be a source of worry, feelings of powerlessness, and difficulties managing a dysphoric experience. In the present study, intense and unmodulated emotions (i.e., anxiety, depression) were more severe in SRa, and this may have contributed to their SR behavior. These data seem aligned with the literature showing similarities between an “avoidance of negative stimuli” SRa cluster (Gonzálvez et al., 2018) and severe SR (Gonzálvez et al., 2020).

Another interesting finding concerns the inhibited and self-critical personality style. Although this personality style was not specific to SRa in the clinical setting, it seemed to characterize at least a sub-group of SRa, given the high mean score in the sample (table 2). This personality style is closely related to internalizing psychopathology, and typically involves feelings of embarrassment and shame in social contexts, high standards, perfectionistic tendencies, and a tendency toward self-criticism. We theorize that, when associated with low adaptive functioning, this personality style and the experience of internalizing symptoms may lead adolescents to refuse school in order to avoid feelings of frustration and devaluation that derive from social and performance challenges.

The antisocial personality style also showed an interesting trend. Although no significant difference emerged between SRa and non-SRa on this variable, the statistical values suggest that further analyses may reveal some discrepancy. On the one hand, the greater antisocial characteristics in SRa seemed opposed to SR, and more closely related to truancy (Kearney, 2003). On the other hand, Kearney et al. (2004) described a functional condition in which youths do not attend school in order to seek attention from primary caregivers. In the latter scenario, students show separation anxiety, want to stay home with their parents, and often display misbehaviors in their attempt to do so. We believe that some antisocial items on the SWAP-A (e.g., “He/she tends to manipulate others’ emotions to get what he/ she wants” [to ensure their physical closeness]; “He/she tends to be unreliable or irresponsible” [and thus fails to comply with their academic duties]) may describe this SR profile, which does not involve severe antisocial behaviors, but shows a manipulative attitude, which distinguishes it from the truancy profile (Kearney et al., 2004). Further research could corroborate or disconfirm this hypothesis.

The narcissistic personality style did not emerge as a differentiating factor between SRa and non-SRa. According to previous research, good self-esteem, high self-value, and a competitive attitude are lacking in SRa. We suggest that this may also be true in clinical settings. Otherwise, introversion, frequent experiences of shame, and low self-esteem (Corville-Smith et al., 1998; Heyne et al., 2020; Matsuura et al., 2020) did not allow us to distinguish between SRa and non-SRa. Potentially, these may represent cross-sectional traits that characterize many psychopathological disorders in the help-seeking population, beyond those presenting with SR (Henriksen et al., 2017).

The link between SR and low adaptive personality characteristics seems clear. The SWAP-A personality health indices are partly composed of items concerning a dimension that is very close to the difficulties involved in attending school; others items refer to balance in emotional expression, personal satisfaction, and the development of appropriate coping strategies. These adaptive characteristics recall the previous matter of negative affectivity reported by SRa. We theorize that SRa have lower levels of global adaptive personality functioning than non-SRa. Although they did not analyze personality adaptive functioning, Gonzálvez et al. (2020) hypothesized that good mental health could protect against problematic school absenteeism. The present data support this hypothesis, expanding the focus beyond psychiatric symptoms to also consider the presence or absence of adaptive personality features. Previous findings suggest that global adaptive functioning (e.g., clinician-rated GAF) may be useful for assessing the impact of symptoms on daily life, amongst SRa (Heyne et al., 2020). On the one hand, we agree with this consideration; on the other hand, we believe that the impact of clinical symptoms may be only one facet of adaptive functioning. Considering this, we introduced SWAP-A indices of personality health into the present study to better investigate adaptive features (e.g., life satisfaction, self-esteem, psychological attitudes toward the self and others).

Although the SRa sample was not large enough to produce reliable statistical results, we drew qualitative considerations based on our clinical experience with SRa and the mean scores of personality features obtained from the SWAP-A—particularly with respect to Q Inhibited Self-Critical (Q IS) and Q Emotionally Dysregulated (Q ED) (table 2). Amongst SRa, average scores for these Q factors reached the threshold for personality pathology (≥ 55T score) and almost satisfied the criteria for personality disorder (≥ 60T score). On the one hand, the Q ED profile describes adolescents who “tend to feel unhappy, depressed or despondent” or whose “emotions tend to spiral out of control, leading to extremes of anxiety, sadness, rage or excitement”; on the other hand, the Q IS profile describes adolescents who “tend to feel ashamed and embarrassed” or “tend to avoid social situations because of a fear of embarrassment or humiliation.” While these profiles have different characteristics and personality styles, they share several emotional difficulties and a propensity for anxiety or mood disorders (Shedler et al., 2014). In the present study, each SRa generated a higher mean score on either Q ED or Q IS. In addition, they each satisfied the SWAP-A criteria for personality disorder. We grouped SRa into sub-groups on the basis of their Q ED and Q IS scores. Fourteen (51.85%) participants were assigned a Q ED profile, showing histrionic, narcissistic, and avoidant-constricted personality traits; 11 (40.74%) were assigned a Q IS profile, associated with avoidant-constricted personality traits; the remaining two (7.41%) presented a Q Avoidant-Constricted style.

We are aware of the limitations of these considerations and the small sample size investigated. Future research should attempt to replicate this study in a larger sample, in order to more accurately describe the personological profiles of SRa. Such profiles seem characterized by emotional features, which are lived and expressed in different ways. These findings could expand the data previously collected by Gonzálvez et al. (2020) about SRa profiles with severely impaired adaptive characteristics.

In conclusion, it seems that emotional dysregulation, as well as self-criticism and a tendency toward self-inhibition, are the main personality characteristics or styles that describe SRa. From this perspective, we suggest that personality traits should be considered in the set of clinical characteristics that can lead to SR (Ingul & Nordahl, 2013).

Conclusion

According to the school policies (Commissione Europea, 2014), within Europe, Italy has one of the highest school dropout percentages of native students (approximately 15%) and the most severe disparity between male and female rates of abandonment. According to the goals of the “Education and Training Monitoring” project, the rate of early school dropout in 2020 should be no more than 10%. In this context, the importance of SR research seems fundamental to counteract this phenomenon, which has several social, economic, and mental health implications. In particular, there is a great need to build knowledge around assessment and interventions for problematic school absenteeism and some specific manifestations of this phenomenon, including SR (Heyne et al., 2020). Further data must be gathered on psychiatric symptoms and interpersonal functioning, in order to better inform us about the specific difficulties of youths presenting with SR. However, it is important to first understand the personality styles that best characterize these symptoms, as such knowledge could be helpfully applied to orient and personalize therapeutic approaches and thereby improve clinical interventions. It may also improve clinicians’ ability to identify youths at greater risk of SR. We still know little about the personality traits related to SR (Heyne et al., 2020). Importantly, future research should investigate these traits from the perspectives of both the relevant youths (e.g., using questionnaires), as well as other important figures, including clinicians, teachers, and parents. Information from these latter sources is still limited, but necessary to improve our knowledge and prevention of school absenteeism (Heyne et al., 2020). Such information could also educate the community (including youths, but mainly parents and teachers) to better identify features that can lead to this problematic behavior, in order to support and guide youths toward mental health centers.

In this vein, the present work has some positive features:

  • It provides a further contribution to school dropout research, focused on SR in the clinical context. The results suggest that SRa in clinical settings do not seem to significantly differ from SRa in school settings. This confirms the specificity of the SR condition (with respect to personality and social functioning), also in the clinical setting.

  • It used clinician-report tools to assess psychiatric and psychological variables. Most prior studies on SR have investigated adolescents’ features using paper-and-pencil self-report tools. However, adolescent patients tend to have poor insight and self-understanding. While this lack of awareness is compatible with and appropriate to their developmental stage, it may limit researchers’ ability to gather accurate information on their personality style and features (Tackett et al., 2013).

Future research directions, linked to the limitations of the study, are outlined below:

  • A larger sample could provide more specific information on personality styles, social functioning, and psychopathology. Moreover, a larger sample of SRa in the clinical context is needed to outline and confirm specific personality clusters, which were only qualitatively and preliminarily identified in the present study.

  • Further investigation of youths’ individual reasons for SR and the link between these reasons and personality features may provide helpful clarity. Tools that investigate possible reasons for SR were not used in the present study due to the lack of an Italian translation of the main self-report measure (e.g., SARS-R; Kearney, 2006). However, this is an important matter to investigate with respect to the entire phenomenon of school withdrawal.

  • While personality features play an evident role in SR, the role that personality plays in the therapeutic setting and scholastic reintegration is unclear. Future studies could better elucidate this issue.

  • Finally, while SR is significantly associated with deficits in social functioning, several questions about this factor remain. Notably, it is not clear with whom SRa have relational problems (e.g., peers, parents), how such problems occur (e.g., avoidance of close relationships, excessive dependance), and under which conditions they determine SR behavior.

The present work aimed at clarifying the main characteristics that mental health specialists should consider in order to prevent SR and intervene in SR cases. In doing so, it also generated meaningful insights into the more complex issue of problematic school absenteeism.

References

  1. American Psychiatric Association (2001). Diagnostic and statistical manual of mental disorders (4th ed.). Milano: Masson ed. [Google Scholar]
  2. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. [Google Scholar]
  3. Askeland, K. G., Bøe, T., Lundervold, A. J., Stormark, K. M., & Hysing, M. (2020). The association between symptoms of depression and school absence in a population-based study of late adolescents. Frontiers in Psychology, 11, 1268. 10.3389/fpsyg.2020.01268 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Auther, A. M., Smith, C. W., & Cornblatt, B. A. (2006). Global Functioning: Social Scale (GF: Social). Glen Oaks, NY: Zucker Hillside Hospital. [Google Scholar]
  5. Berg, I., & McGuire, R. (1971). Are school phobic adolescents overdependent? British Journal of Psychiatry, 119: 167–168. 10.1192/bjp.119.549.167 [DOI] [PubMed] [Google Scholar]
  6. Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600. 10.1017/S0954579400007318 [DOI] [Google Scholar]
  7. Commissione europea/EACEA Education and Youth Policy Analysis/Eurydice/Cedefop (2014). Tackling early leaving from education and training in Europe. Rapporto Eurydice e Cedefop. Lussemburgo: Ufficio delle pubblicazioni dell’Unione europea. [Google Scholar]
  8. Corville-Smith, J., Ryan, B. A., Adams, G. R., & Dalicandro, T. (1998). Distinguishing absentee students from regular attenders: The combined influence of personal, family, and school factors. Journal of Youth and Adolescence, 27: 629–640. 10.1023/A:1022887124634 [DOI] [Google Scholar]
  9. Egger, H. L., Costello, E. J., & Angold, A. (2003). School refusal and psychiatry disorders: A community study. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 797–807. 10.1097/01.CHI.0000046865.56865.79 [DOI] [PubMed] [Google Scholar]
  10. Esch, P., Bocquet, V., Pull, C., Couffignal, S., Lehnert, T., Graas, M., Fond-Harmant, L., & Ansseau, M. (2014). The downward spiral of mental disorders and educational attainment: A systematic review on early school leaving. BMC Psychiatry, 14(1), 237. 10.1186/s12888-014-0237-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gonzálvez, C., Kearney, C. A., Jiménez-Ayala, C. E., Sanmartín, R., Vicent, M., Inglés, C. J., & García-Fernández, J. M. (2018). Functional profiles of school refusal behavior and their relationship with depression, anxiety, and stress. Psychiatry Research, 269, 140–144. 10.1016/j.psychres.2018.08.069 [DOI] [PubMed] [Google Scholar]
  12. Gonzálvez, C., Inglés, C. J., Kearney, C. A., Sanmartín, R., Vicent, M., & García-Fernández, J. M. (2019). Relationship between school refusal behavior and social functioning: A cluster analysis approach. European Journal of Education and Psychology, 12(1), 17–29. https://psycnet.apa.org/doi/10.30552/ejep.v12i1.238 [Google Scholar]
  13. Gonzálvez, C., Díaz-Herrero, Á., Vicent, M., Sanmartìn, R., Pérez-Sànchez, A. M., Garcìa-Fernàndez, & J. M. (2020) School refusal behavior: Latent class analysis approach and its relationship with psychopathological symptoms. Current Psychology: A Journal for Diverse Perspectives on Diverse Psychological Issues. https://psycnet.apa.org/doi/10.1007/s12144-020-00711-6
  14. Havik, T., Bru, E., & Ertesvåg, S. K. (2015). Assessing reasons for school non-attendance. Scandinavian Journal of Educational Research, 59, 316–336. 10.1080/00313831.2014.904424 [DOI] [Google Scholar]
  15. Hamilton, M. A. X. (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology, 32(1), 50–55. 10.1111/j.2044-8341.1959.tb00467.x [DOI] [PubMed] [Google Scholar]
  16. Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56. 10.1136/jnnp.23.1.56 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Henriksen, I. O., Ranøyen, I., Indredavik, M. S., & Stenseng, F. (2017). The role of self-esteem in the development of psychiatric problems: A three-year prospective study in a clinical sample of adolescents. Child and Adolescent Psychiatry and Mental Health, 11, 68. 10.1186/s13034-017-0207-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Heyne, D. A., & Sauter, F. M. (2013). School refusal. In Essau C. A. and Ollendick T. H. (Eds.), The Wiley-Blackwell handbook of the treatment of childhood and adolescent anxiety (pp. 471–517). Chichester: John Wiley & Sons. [Google Scholar]
  19. Heyne, D., Strömbeck, J., Alanko, K., Bergström, M., & Ulriksen, R. (2020). A scoping review of constructs measured following intervention for school refusal: Are we measuring up? Frontiers in Psychology, 11, 1744. 10.3389/fpsyg.2020.01744 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ingul, J. M., & Nordahl, H. M. (2013). Anxiety as a risk factor for school absenteeism: What differentiates anxious school attenders from non-attenders. Annals of General Psychiatry, 12(25), 1–9. 10.1186/1744-859X-12-25 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ingul, J. M., Havik, T., & Heyne, D. (2019) Emerging school refusal: A school-based framework for identifying early signs and risk factors. Cognitive and Behavioral Practice, 26, 46–62. 10.1016/j.cbpra.2018.03.005 [DOI] [Google Scholar]
  22. Janosz, M., LeBlanc, M., Boulerice, B., & Tremblay, R. E. (1997). Disentangling the weight of school dropout predictors: A test on two longitudinal samples. Journal of Youth and Adolescence, 26(6). 10.1023/A:1022300826371 [DOI] [Google Scholar]
  23. Kearney, C. A. (2003). Bridging the gap among professionals who address youths with school absenteeism: Overview and suggestions for consensus. Professional Psychology: Research and Practice, 34, 57–65. 10.1037/0735-7028.34.1.57 [DOI] [Google Scholar]
  24. Kearney, C. A., Lemons, A., & Silverman, J. (2004). The functional assessment of school refusal behavior. The Behavior Analyst Today, 5(3), 275–283. 10.1177/0145445503259263 [DOI] [Google Scholar]
  25. Kearney, C. A. (2006). Confirmatory factor analysis of the School Refusal Assessment Scale-Revised: Child and parent versions. Journal of Psychopathology and Behavioral Assessment, 28(3), 139–144. 10.1007/s10862-005-9005-6 [DOI] [Google Scholar]
  26. Kearney, C. A. (2008). School absenteeism and school refusal behavior in youth: A contemporary review. Clinical Psychology Review, 28(3), 451–471. http://doi:10.1016/j.cpr.2007.07.012 [DOI] [PubMed] [Google Scholar]
  27. Lounsbury, J. W., Steel, R. P., Loveland, J. M., & Gibson, L. W. (2004). An investigation of personality traits in relation to adolescent school absenteeism. Journal of Youth and Adolescence, 33(5), 457–466. https://psycnet.apa.org/doi/10.1023/B:JOYO.0000037637.20329.97 [Google Scholar]
  28. Matsuura, H., Iwasaka, H., Nezu, S., Ota, T., Okazaki, K., Yamamuro, K., & Kishimoto, T. (2020). Influence of self-esteem and psychiatric diagnosis on health-related quality of life in children and adolescents with school refusal behavior. Neuropsychiatric Disease and Treatment, 16, 847. 10.2147/NDT.S246651 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Maynard, B. R., Brendel, K., Bulanda, J. J., Heyne, D., Thompson, A., & Pigott, T. (2015). Psychosocial interventions for school refusal with primary and secondary school students: A systematic review. Campbell Systematic Reviews, 11(12). 10.4073/csr.2015.12 [DOI] [Google Scholar]
  30. Melkevik, O., Nilsen, W., Evensen, M., Reneflot, A., & Mykletun, A. (2016). Internalizing disorders as risk factors for early school leaving: A systematic review. Adolescent Research Review, 1, 245–255. 10.1007/s40894-016-0024-1 [DOI] [Google Scholar]
  31. Nayak, A., Sangoi, B., & Nachane, H. (2018). School refusal behavior in Indian children: Analysis of clinical profile, psychopathology and development of a best-fit risk assessment model. Indian Journal of Pediatrics, 85, 1073–1078. 10.1007/s12098-018-2631-2 [DOI] [PubMed] [Google Scholar]
  32. Niendam, T. A., Bearden, C. E., Johnson, J. K., & Cannon, T. D. (2006). Global Functioning: Role Scale (GF: Role). Los Angeles, CA: University of California, Los Angeles. [Google Scholar]
  33. Okuyama, M., Okada, M., Kuribayashi, M., & Kaneko, S. (1999). Factors responsible for the prolongation of school refusal. Psychiatry and Clinical Neurosciences, 53, 461–469. 10.1046/j.1440-1819.1999.00585.x [DOI] [PubMed] [Google Scholar]
  34. Scholte, R. H. J., & Van Aken, M. A. G. (2006). Peer relations in adolescence. In S. Jackson & L. Goossens (Eds.), Handbook of adolescent development (pp. 175–199). New York, NY: Psychology Press. [Google Scholar]
  35. Seçer, İ., & Ulaş, S. (2020). The mediator role of academic resilience in the relationship of anxiety sensitivity, social and adaptive functioning, and school refusal with school attachment in high school students. Frontiers in Psychology, 11. 10.3389/fpsyg.2020.00557 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Shedler, J., Westen, D., & Lingiardi, V. (2014). La valutazione della personalità con la SWAP-200. Raffaello Cortina Editore.
  37. Tackett, J. L., Herzhoff, K., Reardon, K. W., Smack, A. J., & Kushner, S. C. (2013). The relevance of informant discrepancies for the assessment of adolescent personality pathology. Clinical Psychology: Science and Practice, 20(4), 378–392. 10.1111/cpsp.12048 [DOI] [Google Scholar]
  38. Vander Stoep A., Weiss, N. S., Kuo, E. S., Cheney, D., & Cohen, P. (2003). What proportion of failure to complete secondary school in the US population is attributable to adolescent psychiatric disorder? The Journal of Behavioral Health Services & Research, 30(1), 119–124. 10.1007/BF02287817 [DOI] [PubMed] [Google Scholar]
  39. Westen, D., Dutra, L., & Shedler, J. (2005). Assessing adolescent personality pathology. The British Journal of Psychiatry, 186(3), 227–238. 10.1192/bjp.186.3.227 [DOI] [PubMed] [Google Scholar]
  40. World Health Organization. (2007). International statistical classification of disease and health related problems. ICD-10. Geneva.
  41. Young, R. C., Biggs, J. T., Ziegler, V. E., Meyer, D. A. (1978). A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry, 133, 429-35. doi: 10.1192/bjp.133.5.429 [DOI] [PubMed] [Google Scholar]

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