Open access peer-reviewed chapter
Variable.
Abstract
Students with severe mental disorders (SMDs) are a vulnerable population with higher risks of early school dropout than the general population. Our aim has been to define psychosocial factors of students aged 12–18 years who have been diagnosed with severe mental disorders. So, we have defined the psychosocial factors of a group of students aged 12 to 18 years who have been diagnosed with a SMD. We have made the selection of the sample through an intentional nonprobability sampling. One hundred and nine cases of students were analyzed. We have analyzed the evolution of the student throughout their academic history until the moment in which they are hospitalized in serious condition by means of an exploratory factor analysis, with the application of the KMO sample adequacy of 0.776 and the significance of Bartlett’s test of sphericity p < .001, we have obtained a high correlation between the variables. The factors obtained are study limitations, symptomatology representation, study facilitators, other limitations. The results show that it is necessary to take into account the conditions that prevent them from permanence, inclusion, coexistence, and educational achievement. Likewise, symptomatic expression and family support are key elements in improving the educational process of pupils with SMD. These factors allow us to infer pedagogical practices that are more appropriate to their needs.
Keywords
- mental health
- school dropout
- educational achievement
- adolescence
- psychosocial factors
1. Introduction
The impact severe mental disorders (SMDs) have on a student acquires great relevance in the adolescent’s life. This is because of the repercussion it has on academic performance and the construction of a personal project while hindered by the disorder [1]. We emphasize the importance of this study, given that it aims to make it known that these students with mental disorders, in order for them to remain in the educational system, must receive adequate attention that meets their needs. It is a vulnerable population that presents greater risks of early school dropout than the general population [2]. It is pertinent to study in-depth lines of work that respond to such a specific need [3].
Before going any further, we would like to delimit the concept of SMD, so we can understand the profile of the studied population and some of the elements that make this population a homogeneous observation group. The severe mental disorders would be those disorders that “due to their severity, seriously compromise evolution, learning, personal development and the social and labor insertion of those children or adolescents who suffer from it. ….. that mental disorder of long duration and involving a variable degree of disability and social dysfunction” [4].
We found different perspectives [5], but they all could be synthesized in the idea that a serious mental disorder is that whose symptomatology affects very severely on different areas of the person and for a long time.
It is estimated that 20% of adolescents suffer from some type of mental disorder [6]. We know mental health problems account for 16% of total adolescent health problems. Also, half of mental health problems begin at the age of 14 years, and most of them go undetected or untreated. The same authors point out that suicide is the third cause of death between ages 15 and 19 years, as a consequence of not addressing mental health problems during the transition from adolescence to adulthood [7]. This limits the opportunities to develop a fulfilling life as an adult. This population segment is at a disadvantage regarding their participation, permanence, and promotion in the educational field. Adopting support measures that respond to their educational needs should compensate this situation.
According to the United Nations Children’s Fund [8], children who suffer from mental health problems are among the most vulnerable population groups. For an adolescent with a SMD, academic success is central and structural for the construction of their personal project, both from an academic and therapeutic point of view. Maintaining the relationship and staying in school allow students with SMD to have a reference of fundamental normality, so as to make improvements in other areas of their lives.
Some publications suggest that students with externalizing disorders are more likely to obtain poor academic results. This same publication points out that the economic factors, the age of onset of the mental disorder, academic performance, and family support are among some of the factors that are present in students with severe mental disorders [9]. Other studies indicate that groups of students with higher levels of emotional strength and low levels of distress obtain higher grades, have a greater prosocial contribution to the community, and show greater satisfaction with life [10].
The relationship between mental health and psychosocial factors seems clear [11]. Psychosocial factors play an important role in the adherence to interventions of the population with severe mental disorders. They could also play it on their academic results. Knowing the psychosocial factors of students with severe mental disorders could help us to personalize interventions [12].
We understand that, in order to develop educational practices appropriate to the needs of students with a SMD [13], understanding and knowing the psychosocial characteristics of this class of students is mandatory. This is how the European Education Information Network of the European Union warned in 2017 that one in eight children has a diagnosed mental disorder [14]. These same authors argue that 50% of mental health problems in adulthood begin to take effect before the age of 15 years, in adolescence. From this reality, it is necessary to deepen the development of educational practices that respond to many adolescents who are in a situation of greater vulnerability.
Based on the results obtained in a previous research carried out by us [15], and on the research in the scientific literature, we have used 18 variables to analyze the observed reality. We want to show the meanings attributed to each variable, since they will serve to interpret and help us make decisions in the factor analysis adjustment that we explain in the following section.
Thus, when we have observed the family accompaniment, we understand the fundamental role the family has both in the genesis and the treatment of the disorder [16]. So, depending on how the family support developed, the family will therefore be either a protective or a risk factor for students in a situation of SMD. When we talk about a family providing positive support, we refer to those families that understand their children’s clinical situation and, therefore, make good use of training guidelines favoring a healthy upbringing. We could differentiate between those families that play an abandonment and negligent role, not very favorable to the children’s rehabilitation process. At the other extreme, we would find families with an excessively care role and whose accompaniment is overprotective. In either case, we would be referring to families with difficulties in establishing a healthy parental-filial relationship.
When we analyzed the type of linkage with the school, we considered that it was a good linkage when maintaining the relationship and staying in school allow students with severe mental disorders to maintain a reference of fundamental normality that propitiates improvements in other areas of their lives. A proper bond with the school describes not only permanence and regular assistance – elements that are closely related –, but the grade the student feels and perceives the context with [3]. Failure to establish stable and healthy links with teachers, the educational center and the classmates are directly related with low self-esteem and could have a direct impact in the student’s academic performance [17]. We cannot forget that the perception that the rest of the educational community has and perceives of students with SMD would be included in the relationship with the educational center. This is how some research indicates that young people have negative attitudes toward their peers with mental health difficulties [18].
With the definition of this variable, we talk about lack of conflicts and a good relationship with teachers and peers. All this defines a contextual framework that allows students to express themselves with emotional tranquility. This is fundamental for their emotional wellness and the avoidance of subclinical or clinical symptoms. So is some authors see the inability to bond as a determining risk factor in obtaining mental health [19].
When analyzing academic success, we cannot forget that the children with mental health problems obtain worse academic results [20]. We also know there is an association between mental health problems and persistent truancy. This relationship is greater in those students who showed externalizing symptoms, since they obtain worse academic results [21]. So, when talking about academic success, we are referring to obtaining good academic results. It is mandatory to pass all subjects, except two, to be promoted to the next course. When a child makes through, we considered they have achieved academic success. When a student fails more than two subjects, we consider they are not in a situation of academic success. Academic success can be interpreted in multiple ways. It depends on where the light is pointing. We have not wanted to establish references in the scientific literature. We wanted to define what the concept means to us. That has been our way of observing and looking for the data in the consulted reports.
On the other hand, we also wanted to analyze the degree of academic promotion of the population analyzed. In this sense, some research shows the negative relationship between academic performance and psychiatric disorders, whether externalizing or internalizing ones [22]. Therefore, the possibility of repeating an academic year seems to be a conditioning element for the population studied. We have taken as a unit of temporal analysis the moment of the analytical work of the present investigation. This circumstance could have happened more times. Anyway, we understand that when a member of the sample repeated a school year at last once prior to hospitalization, the variable is marked as positive.
Regarding the continuity of learning processes and school attendance of these students, we found data indicating that the degree of truancy in students with mental disorders is higher than in the rest of the population [23]. Many factors define the situation of an absconding student. However, we would like to stress how important the degree of connection between the child and their school is. When a child feels a high-level bond with the school, it is because they also feel understood and included. So, for us, the degrade of truancy is a consequence of the engagement level with the educative center. Moreover, we think an avoidant behavior is an indication that something is not working as it should.
We have considered that the previous academic history of students with severe mental disorders also becomes a conditioning element for the improvement of these students. Thus, when we have considered this variable, we want to highlight one element that could be present in a student with a SMD [15]. Thus, there is a high chance they have bad previous academic records. This circumstance is present specially in externalizing disorders [9].
We also know that curricular backwardness affects many students with a SMD [24]. This variable defines the level of educative competence of a student in relation to their age. When a student has at least 1 year of curricular lag, we believed it pertinent to conceptualize the variable as positive.
Among the risk factors that predispose a student toward an early school dropout, there is the motivation toward study [25]. This is important for clinical improvement, as school expectations are a fundamental protective factor for mental health [26]. We considered this as a qualitative dichotomous variable.
It is also common for students with SMD to have poor study habits. This is because the disorder interrupts any process. Thus, when the student gets in a dire situation, they find it very difficult to maintain a routine in any activity [27].
In relation to limited conceptual and procedural power and/or cognitive difficulties, we can observe that this obstacle to participate in classroom dynamics predisposes students with SMD toward greater vulnerability in the educational context. This is because they lack the necessary skills to function adaptively in the methodological dynamics of a mainstream classroom [9].
We would also like to highlight the attentional difficulties of these pupils. In this regard, we will not only refer to the disorder with the highest level of attention impairment, as is the attention-deficit hyperactivity disorder. Attention difficulties are a circumstance present in many SMDs. This is because, whether the disorder is an externalizing or internalizing one, the moment of the disorder onset marks the difference [28]. Being in a severe moment could provoke attention difficulties due to circumstances such as problems arising from the disorder and medication, among others.
On the other hand, it seems relevant to us to differentiate between internalizing and externalizing disorders, since the way they are represented in the educational context is different. Thus, internalizing disorders collide less with interpersonal relationships, since they do not contribute to the distortion of coexistence in the educative center. On the other hand, externalizing disorders clash frontally with the school life in an educational center [9, 15]. As these disorders generate more disruption in the classroom, the student’s struggle with this type of disorder challenges more people and is more evident. So, we could say that internalizing disorders can go unnoticed and externalizing disorders become apparent more easily. Finally, we will point out that internalizing disorders, despite going unnoticed in most cases, are sometimes expressed with greater implosion on social networks. Hence, a minor who goes unnoticed in the educational center might be sharing on social sites such material that could compromise them before the educational community even more than an externalizing disorder. In our research when we found one of these cases, we conceptualized it as a problem of coexistence.
We also found it interesting to focus on relational style, as the learner may be more or less inhibited in the way he/she relates to others. It is common to find a greater degree of uninhibited in behavioral disorders. On the other hand, internalizing disorders do not always manifest themselves in a little visible way [29]. Because of this, the inhibited-uninhibited categorization could help to clarify how the mental disorder is represented in the educational center.
In the scientific literature, we also find data that leads us to consider the moment at which the disorder was detected, since when detection is early the prognosis may improve. In this sense, it is known that the prodrome symptoms of SMD begin to present themselves during adolescence [21]. For this reason, early detection could be an element that helps reduce the impact of the disorder on the child’s vital development.
From here, we could analyze when educational problems start. Some studies show vulnerability to clinical symptoms already in the early school years [30]. We also know mental disorders in early childhood are three times as likely in children who present misconduct at the age of 5 years, and around seven times as likely in children with special educational needs [31]. That is, the moment of detection could determine if the disorder occurs one way or the other, as early detection allows intervening in more preventive and less palliative terms [32].
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2. Aim
Having analyzed the conceptual elements on which we have decided to focus our research, we set ourselves the following aim, to define psychosocial factors of students aged 12–18 years who have been diagnosed with a severe mental disorders.
2.1 Materials and methods
2.1.1 Sample selection
For the selection of the sample, we have used an intentional sampling criterion [33]. One hundred and nine cases of students with SMD in the educational field have been analyzed. We have analyzed both internalizing and externalizing serious mental disorders. They range in age from 12 to 17 years old. The mean and mode is 15 years of age. Forty-five percent of them are female and 55% are male.
As inclusion criteria, we note that all cases have been treated for an average of 11 months in a Day Hospital for adolescents with subacute symptomatology. In this Day Hospital, they have received both clinical care and formal educational care. It is therefore a specialized center in which the patients are also students and fulfill this dual role. To be admitted to this Day Hospital, a public Mental Health Centre under DSM-5 and ICD-10 criteria must previously diagnose all patients. All patients have a diagnosis considered a serious mental disorder due to its repercussion and durability. All patients were between 12 and 17 years of age. All of them were in school and doing their studies. Those cases that, despite meeting the criteria indicated, have not been able to access all the information necessary for their analysis because it is not included in their file have been excluded.
The time of the investigative analysis has been retrospective. Therefore, it is a longitudinal study in time that we have analyzed in the present with data from the past. We have analyzed the evolution of the student throughout his academic history until the moment in which he/she is hospitalized in serious condition. We have not taken into account the time after hospitalization. We have performed the analysis with the data collected up to the time of the onset of hospitalization. The analysis of the sample was carried out during the 2018–2019 school year in the Madrid Regional Authority.
We have taken into account, at all times, ethical principles consistent with scientific honesty and the protection of privacy in the cases analyzed [34]. The cases analysis was conducted by reviewing educational records opened for students hospitalized in a day care hospital working with adolescents with a SMD. In our research, we used secondary documents, not the real persons. We kept the anonymity of the cases throughout the whole research and did not ever use any real names. All cases were assigned a code so as to preserve anonymity. The educational director of the specialized center where the students are hospitalized authorized access to secondary reports. The reports were anonymized prior to submission for further analysis. We evaluated the data using a working matrix of a data sheet, which was the basis for analysis with SPSS. In this work matrix, we have added the data obtained from the reading of educational reports.
To conclude this section, we would like to point out, in relation to the conflict of interest, that the data collection was carried out by a member of the educational team working in the Day Hospital specialized in the care of students with SMD where the fieldwork was conducted. We consider that this element provided analytical depth and global vision to the development of the work.
2.1.2 Instrument
In order to further develop our study, we drew upon a previous research. In that research, carried out under qualitative methodology, we inferred the main characteristics that define students with a SMD. This research used triangulation of in-depth interviews, questionnaires, and case analysis. More information can be found in [15]. This was the first step, but we could feel this investigation as the beginning of something that needed more depth of analysis. We also saw the need to use a larger sample with which to obtain greater rigor and statistical value. With this previous research, we verified that the model obtained was pertinent to the cases analyzed via a qualitative methodology. So, based on the data objected to in this previous research, we understand the present research have construct validity. It is also reliable, since it measures the relationship between variables that it claims to measure with statistical rigor – we explain this element in the next section. Once the sample is shaped, the next step was to define the variables to be used. Then, we have constructed a meaning for the variables, and we have established an observation map for completing the work matrix needed to develop this research. In the following section, we have read the educational reports of the selected students. The analyzed information was recorded in said files. With the reading of these reports, we have completed an ad hoc matrix. This statistical matrix synthesizes the description of the 109 cases observed, allowing us to reduce the complexity of the cases in qualitative terms to measurable and observable units. The records are made up of psycho-pedagogical reports and educational longitudinal information. This information provides data related to the evolution of the student throughout their educational career so far. Then, in relation to reliability, we affirm that we were able to access all the indicated information. Regarding the reliability of the coding of the observed variables, we have been looking for each variable in the report of each student. In the work matrix used, we marked whether it was met or not. Therefore, all the variables have been dichotomous, except for variable 14 – age – and variable 17 – when did the academic problems begin?. When the report did not provide the information needed, the case was dismissed.
Finally, the variables used and their statuses were as follows: (Table 1).
| V1. | Accompanying family. We conceptualized this as a qualitative dichotomous variable: Family that accompanies positively in the therapeutic process of the child versus family that does not accompany positively. |
| V2. | Good bonding with the educational center. We conceptualized this as a qualitative dichotomous variable: has good school bonding versus poor school bonding. |
| V3. | Academic success. The educational norm tells us that it is necessary to pass all but two subjects in order to move on to the next year. When the child is in this situation, we understand that he/she has achieved academic success. When the student has failed more than two subjects, we consider that he/she was not in a situation of academic success. We conceptualized this as a qualitative dichotomous variable. |
| V4. | Sex. We have differentiated between female and male students. We conceptualized this as a qualitative dichotomous variable. |
| V5. | They repeated some grade. We conceptualized this as a qualitative dichotomous variable: Repitieron algún grado frente a no repetir. |
| V6. | Truancy/irregular assistance. We conceptualized this as a qualitative dichotomous variable: in the case of a student whose class attendance is not continued. |
| V7. | Bad previous academic history. We conceptualized this as a qualitative dichotomous variable: bad previous school history versus good previous school history. |
| V8. | Curricular lag. We considered this as a qualitative dichotomous variable: we have used the curricular lag as a variable that defines the level of educative competence that the student has in relation to their age. If the student has at least 1 year of curricular lag at least, we have understood that was pertinent to conceptualize the variable like positive. |
| V9. | Good motivation toward study. We considered this as a qualitative dichotomous variable: has good study motivation versus does not have good study motivation |
| V10. | Study habits. We considered this as a qualitative dichotomous variable: has study habits versus does not have study habits. |
| V11. | Limited conceptual and procedural power/cognitive difficulties. Students with less procedural and conceptual skills have more difficulties and accumulated negative experiences in the educational context. This incidence in basic elements to participate in the dynamics of classroom-class predisposes students with serious mental disorders to have greater vulnerability in the educational context. This is because they do not meet the necessary skills to function in the methodological dynamics of a classroom. We considered this as a qualitative dichotomous variable. |
| V12. | Attention difficulties. The attention difficulties are a circumstance present in many severe mental disorders. It is because, whether the disorder is externalizing or internalizing, the moment of the disorder marks the different. Being in a severe moment could involve having difficulties with attention due to circumstances such as problems arising from the disorder and medication, among others. We considered this as a qualitative dichotomous variable. |
| V13. | Coexistence issues. We considered this as a qualitative dichotomous variable: presents coexistence problems versus does not present coexistence problems. |
| V14. | Age. We considered this as a quantitative scale variable. |
| V15. | Relational style (inhibited/uninhibited). We considered this as a qualitative dichotomous variable. |
| V16. | Early detection. Early detection is a fundamental element to be able to conduct a good prognosis and minimize the impact of the disease on adulthood. We considered this as a qualitative dichotomous variable: has an early detection record versus does not have an early detection record. |
| V17. | Onset of the educational issues. We considered this as a qualitative polychotomous variable. The ranges used were primary education, 1st year of secondary education, 2nd year of secondary education, 3rd year of secondary education, and 4th year of secondary education. |
| V18. | Disorder typology. We conceptualized this as a qualitative dichotomous variable: externalizing versus internalizing. |
Table 1.
2.2 Data collection and analysis procedure
A descriptive analysis of the quantitative variables yields the following data:
After collecting all the information on the work matrix, and identifying the items that apply to each participant in the sample, we used the exploratory factor analysis to conduct our research (Table 2). As this is a multivariate method that allows for reducing the dimensionality of a problem in a set of underlying variables, we considered it adequate for obtaining factors as a set of variables that allow us to understand the relationships between the variables previously described. Therefore, we looked for those factors that explain most of the common variance.
| Statistics | |||
|---|---|---|---|
| Age | Academic problems begin | ||
| N | Valid | 109 | 109 |
| Missing | 0 | 0 | |
| Mean | 14,69 | 1,05 | |
| Std. deviation | 1296 | ,937 | |
Table 2.
Academic problems begin.
We used an exploratory factor analysis with SPSS Statistics Mac software (v.20.0.0). For this analysis, we used all the variables that make up the study. After analyzing the correlation matrix, we decided on removing variables V4-Sex, V12-Difficulties of attention, and V16-Early detection. We noticed that, by removing them, we obtained a more adequate model, regardless of the values obtained from correlation, the level of Kaiser-Meyer-Olkin sample adequacy, and the significance of the Bartlett sphericity test. To make this decision, we have factored the analysis of communalities and the total value of the variance explained. Regardless of the variables used, we obtained communities close to and greater than .6, as well as better values for total variance. Also, we found greater explanatory coherence according to the theoretical model used, since our intention has been to reach the greatest possible objectivity through a reflective process in which, sometimes, the researcher has to distance himself/herself from the reality constructed by himself/herself [35]. We have ruled out that the variables can cause problems with collinearity because there are no values equal to or greater than 0.9 in the correlation matrix [36]. Finally, after dispensing with the variables indicated, we obtained a Kaiser-Meyer-Olkin sample adequacy result of .776 and significance in the Bartlett test p < .001. Since the Kaiser-Meyer-Olkin test is greater than 0.7, we can count this as a good value [37]. Bartlett’s sphericity test is <.05 and therefore significant. It indicates there are sufficient correlations between the variables to proceed. Given this data, we could say the factor analysis is relevant (Table 3).
| Kaiser-Meyer-Olkin measure of sampling adequacy | .776 | |
|---|---|---|
| Bartlett’s test of sphericity | Approx. Chi-square | 721.333 |
| df | 105 | |
| Sig. | .000 | |
Table 3.
Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity.
Analysis of the anti-image correlation matrix through the sample adequacy measure shows that, once said variables are eliminated and we work with the chosen model, all the variables show high correlation. We have used the anti-image correlation matrix analysis because it is the most suitable for principal component analysis, and this is the method we have used. This method gives us a representation of variance for each variable explained by the factors. The data obtained in the table of communalities offer adequate extraction values, since they are close to or above 0.6 (Tables 4 and 5) [38].
| V1 | V2 | V3 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V13 | V14 | V15 | V17 | V18 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| V1 | .615 | −.164 | −.032 | −.018 | −.041 | −.107 | .090 | −.161 | .048 | −.115 | −.026 | .096 | .015 | .130 | −.065 | |
| V2 | −.164 | .549 | −.109 | −.017 | .189 | .048 | −.019 | .072 | −.063 | .068 | .064 | .041 | −.078 | −.063 | .032 | |
| V3 | −.032 | −.109 | .429 | .081 | .018 | .083 | −.044 | −.068 | −.094 | .025 | −.036 | .013 | .000 | .045 | −.067 | |
| V5 | −.018 | −.017 | .081 | .540 | .007 | −.091 | −.025 | .031 | .037 | .000 | −.005 | .146 | −.052 | −.086 | −.066 | |
| V6 | −.041 | .189 | .018 | .007 | .742 | −.061 | −.027 | −.038 | .042 | .071 | −.008 | .031 | −.061 | .041 | .115 | |
| V7 | −.107 | .048 | .083 | −.091 | −.061 | .289 | −.155 | .060 | −.033 | .026 | .022 | .012 | .032 | −.105 | .003 | |
| V8 | .090 | −.019 | −.044 | −.025 | −.027 | −.155 | .324 | −.003 | .048 | −.211 | .010 | −.057 | −.001 | .055 | −.007 | |
| V9 | −.161 | .072 | −.068 | .031 | −.038 | .060 | −.003 | .397 | −.186 | .077 | .037 | .039 | −.048 | −.091 | −.010 | |
| V10 | .048 | −.063 | −.094 | .037 | .042 | −.033 | .048 | −.186 | .418 | −.105 | −.015 | .001 | −.004 | .063 | .081 | |
| V11 | −.115 | .068 | .025 | .000 −005 | .071 | .026 | −.211 | .077 | −.105 | .487 | .015 | .054 | −.078 | −.084 | .009 | |
| V13 | −.026 | .064 | −.036 | −.005 | −.008 | .022 | .010 | .037 | −.015 | .015 | .353 | .023 | .161 | −.146 | −.162 | |
| V14 | .096 | .041 | .013 | .146 | .031 | .012 | −.057 | .039 | .001 | .054 | .023 | .698 | .003 | −.191 | −.043 | |
| V15 | .015 | −.078 | .000 | −.052 | −.061 | .032 | −.001 | −.048 | −.004 | −.078 | .161 | .003 | .447 | −.036 | .094 | |
| V17 | .130 | −.063 | .045 | −.086 | .041 | −.105 | .055 | −.091 | .063 | −.084 | −.146 | −.191 | −.036 | .468 | .037 | |
| V18 | −.065 | .032 | −.067 | −.066 | .115 | .003 | −.007 | −.010 | .081 | .009 | −.162 | −.043 | .094 | .037 | .436 | |
| V1 | .632a | −.283 | −.062 | −.032 | −.061 | −.253 | .201 | −.327 | .094 | −.210 | −.057 | .147 | .029 | .243 | −.125 | |
| V2 | −.283 | .795a | −.224 | −.031 | .296 | .120 | −.045 | .155 | −.132 | .132 | .145 | .067 | −.157 | −.124 | .065 | |
| V3 | −.062 | −.224 | .882a | .168 | .032 | .234 | −.117 | −.164 | −.223 | .055 | −.092 | .024 | −.001 | .100 | −.155 | |
| V5 | −.032 | −.031 | .168 | .878a | .011 | −.230 | −.060 | .066 | .079 | .000 | −.012 | .238 | −.107 | −.171 | −.137 | |
| V6 | −.061 | .296 | .032 | .011 | .672a | −.131 | −.055 | −.070 | .075 | .118 | −.015 | .044 | −.106 | .069 | .203 | |
| V7 | −.253 | .120 | .234 | −.230 | −.131 | .799a | −.507 | .178 | −.096 | .071 | .069 | .026 | .089 | −.286 | .008 | |
| V8 | .201 | −.045 | −.117 | −.060 | −.055 | −.507 | .741a | −.009 | .131 | −.532 | .031 | −.120 | −.002 | .141 | −.020 | |
| V9 | −.327 | .155 | −.164 | .066 | −.070 | .178 | −.009 | .799a | −.458 | .174 | .100 | .075 | −.115 | −.210 | −.024 | |
| V10 | .094 | −.132 | −.223 | .079 | .075 | −.096 | .131 | −.458 | .820a | −.233 | −.040 | .002 | −.010 | .142 | .189 | |
| V11 | −.210 | .132 | .055 | .000 | .118 | .071 | −.532 | .174 | −.233 | .646a | .037 | .092 | −.168 | −.176 | .019 | |
| V13 | −.057 | .145 | −.092 | −.012 | −.015 | .069 | .031 | .100 | −.040 | .037 | .742a | .046 | .405 | −.358 | −.413 | |
| V14 | .147 | .067 | .024 | .238 | .044 | .026 | −.120 | .075 | .002 | .092 | .046 | .748a | .005 | −.335 | −.078 | |
| V15 | .029 | −.157 | −.001 | −.107 | −.106 | .089 | −.002 | −.115 | −.010 | −.168 | .405 | .005 | .803a | −.079 | .212 | |
| V17 | .243 | −.124 | .100 | −.171 | .069 | −.286 | .141 | −.210 | .142 | −.176 | −.358 | −.335 | −.079 | .735a | .081 | |
| V18 | −.125 | .065 | −.155 | −.137 | .203 | .008 | −.020 | −.024 | .189 | .019 | −.413 | −.078 | .212 | .081 | .762a | |
| Measures of sampling adequacy (MSA) | ||||||||||||||||
Table 4.
Matrix of anti-image correlations.
| Initial | Extraction | |
|---|---|---|
| Accompanying family | 1.000 | .672 |
| Good bonding with the educational center | 1.000 | .583 |
| Academic success | 1.000 | .659 |
| They have repeated some grade | 1.000 | .588 |
| Truancy/irregular assistance | 1.000 | .657 |
| Bad previous academic history | 1.000 | .771 |
| Curricular lag | 1.000 | .730 |
| Good motivation toward study | 1.000 | .570 |
| Study habits | 1.000 | .614 |
| Reduced conceptual and procedural power/cognitive difficulties | 1.000 | .692 |
| Coexistence problems | 1.000 | .769 |
| Age | 1.000 | .661 |
| Relational style | 1.000 | .717 |
| Disorder typology | 1.000 | .761 |
| Onset of the educational issues | 1.000 | .570 |
Table 5.
Commonalities obtained through principal component analysis.
Extraction method: principal component analysis.
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3. Results
To determine the number of factors extracted, we took into account the initial self-values, the total of the explained variance, and the theoretical model. Looking at Table 6, we find that all four factors selected are above 1. Therefore, we have performed the factor analysis extracting four factors. Although with other models we obtained a higher percentage of the total variance explained, we consider the chosen model does a better job at explaining its representation from a theoretical point of view.
| Initial autovalues | Extraction sums of squared loadings | Rotation sums of squared loadings | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Component | Total | % of variance | Cumulative% | Total | % of variance | Cumulative % | Total | % of variance | Cumulative % |
| 1 | 4.880 | 32.536 | 32.536 | 4.880 | 32.536 | 32.536 | 3.238 | 21.584 | 21.584 |
| 2 | 2.607 | 17.381 | 49.916 | 2.607 | 17.381 | 49.916 | 2.827 | 18.846 | 40.429 |
| 3 | 1.355 | 9.034 | 58.950 | 1.355 | 9.034 | 58.950 | 1.997 | 13.315 | 53.745 |
| 4 | 1.172 | 7.813 | 66.763 | 1.172 | 7.813 | 66.763 | 1.953 | 13.019 | 66.763 |
| 5 | .911 | 6.073 | 72.836 | ||||||
| 6 | .764 | 5.096 | 77.932 | ||||||
| 7 | .627 | 4.181 | 82.113 | ||||||
| 8 | .506 | 3.372 | 85.485 | ||||||
| 9 | .445 | 2.964 | 88.449 | ||||||
| 10 | .433 | 2.885 | 91.333 | ||||||
| 11 | .343 | 2.289 | 93.622 | ||||||
| 12 | .300 | 2.001 | 95.623 | ||||||
| 13 | .270 | 1.799 | 97.422 | ||||||
| 14 | .231 | 1.540 | 98.962 | ||||||
| 15 | .156 | 1.038 | 100.000 | ||||||
Table 6.
Total variance explained with the first four factors.
Extraction method: principal component analysis.
In other words, we weighted the part of the common variance that enables us to explain these factors with greater theoretical sense. We therefore seek an interpretation that combines parsimony and plausibility [38].
The total accumulated variance value is 66.763%, which, together with the criteria described earlier, allows us to think about the model’s adequacy. As the value obtained is above .60%, we consider it appropriate [37] (Table 6).
The extraction method is the principal component analysis, while rotation type is orthogonal with the varimax method. We see each factor is represented by three or more variables; therefore, we consider that the model obtained meets moderate conditions for interpretation (Tables 7 and 8) [38].
| Component | ||||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| Accompanying family | −.439 | .011 | .652 | −.233 |
| Good bonding with the educational center | −.624 | .090 | .263 | .341 |
| Academic success | −.736 | −.262 | .146 | .166 |
| They repeated some grade | .641 | .198 | .351 | −.119 |
| Truancy/irregular assistance | .201 | .400 | −.268 | −.620 |
| Previous academic history | .777 | .331 | .238 | −.034 |
| Curricular lag | .650 | .462 | .248 | .181 |
| Motivation toward study | −.751 | .016 | .075 | .001 |
| Study habits | −.734 | .161 | .092 | .202 |
| Reduced conceptual and procedural power/cognitive difficulties | .365 | .555 | .388 | .316 |
| Coexistence problems | .434 | −.739 | .184 | −.017 |
| Age | .411 | −.178 | −.452 | .506 |
| Relational style | −.376 | .737 | −.113 | .138 |
| Disorder typology | .327 | −.738 | .331 | −.016 |
| Onset of the educational issues | .662 | −.084 | −.034 | .353 |
Table 7.
Matrix of main components.
Extraction method: principal component analysis. a. Four components extracted.
| Component | ||||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| Accompanying family | −.005 | .044 | .275 | |
| Good bonding with the educational center | −.167 | −.276 | .262 | |
| Academic success | −.509 | −.037 | .287 | |
| They have repeated some grade | .205 | −.274 | .061 | |
| Truancy/irregular assistance | .051 | −.286 | .135 | |
| Bad previous academic history | .101 | −.334 | −.108 | |
| Curricular lag | −.079 | −.125 | −.155 | |
| Good motivation toward study | −.288 | .369 | .373 | |
| Study habits | −.327 | −.420 | .279 | |
| Reduced conceptual and procedural power/cognitive difficulties | −.241 | .152 | −.003 | |
| Coexistence problems | .033 | −.023 | −.137 | |
| Age | .069 | .125 | .056 | |
| Relational style | .069 | .121 | .092 | |
| Disorder typology | .038 | .085 | .009 | |
| Onset of the educational issues | .467 | .289 | −.025 | |
Table 8.
Rotated component matrix.
Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. a. Rotation converged in six iterations.
We will focus our analysis on the rotated components matrix and not on the main components matrix. In this way, we obtain what is called the simple structure principle, achieving a better scientific interpretation of the obtained factors, since we give more importance to the variables that obtain greater weight in the obtained factors [39].
Analyzing the matrix of rotated components, we can see factor number 1 is formed by these variables:
V5. They have repeated some course.
V7. Bad previous school history.
V8. Curricular lag.
V9. Motivation toward study.
V11. Reduced conceptual and procedural power/cognitive difficulties.
We called this factor
Factor number 2, which we will call the
V18. Type of disorder.
V15. Relational style.
V13. Coexistence problems.
We can observe that, in all three cases, the weight of the variables in the factor is higher than 0.8, so we consider that their contribution is relevant.
Factor number 3, which we will call
V2. Good bonding with the educational center.
V3. Academic success.
V6. Truancy. Irregular assistance.
V10. Study habits.
Of the four variables that make up the factor, the best representations are V6 and V2. We can see the other variables have a lesser weight.
Finally, factor number 4 will consist of these variables:
V1. Accompanying family.
V14. Age.
V17. Onset of the academic issues.
We called this factor
| They have repeated some course Bad previous school history Curricular lag Motivation toward study Reduced conceptual and procedural power/cognitive difficulties | Type of disorder Relational style Coexistence problems | Good bonding with the educational center Academic success Truancy. Irregular assistance Study habits. | Accompanying family Age Onset of the academic issues |
Table 9.
Factors.
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4. Discussion and conclusions
Beyond the mental disorder and knowing how it is represented, we have tried to understand how it is present in the educational environment. We believe that knowing how MSD is represented in the 109 cases analyzed can help us to deepen the understanding and establishment of compensation mechanisms in preventive terms [3]. Only by understanding and knowing the elements that define a reality in contextual terms, we can develop and implement educational measures that are empathetic [40].
Therefore, we wanted to look at the common elements the students with mental disorders show when in a severe condition. We used 18 variables to test them using the quantitative methodology. In the interpretation of the exploratory factor analysis that we have carried out, we have decided to dispense with the variables V4-Sex, V12-Attention Difficulties, and V16-Early Detection. Using these three variables, the factorial models obtained were less statistically consistent. Therefore, in making the decision not to include the three variables we have dispensed with in the proposed factor analysis, we have taken into account both the statistical data obtained and the theoretical framework. We put theory and data together to make a decision. In this way, we were able to obtain a more consistent factor model.
We observed that the variable V12 expresses a symptomatic value that mainly affects externalizing disorders. We were also able to observe that without this variable in the factor analysis, we obtained better statistical results. So given that its contribution to the model generated many doubts from a statistical point of view, and after reviewing the theoretical framework, we decided to eliminate this variable.
Regarding variable V16, we can see it defines a time frame from the clinical perspective. The analysis we are doing focuses on the educational field. In this sense, it must be said we already had another variable covering the moment in which the disorder visibly affects the educational environment, and that is variable V17 – when academic problems start. Thus, given the little representativeness obtained for the model by incorporating variable V16 and the weight of the interpretation carried out under the theoretical framework, we decided to continue without it.
Therefore we find that, when in a serious situation, students with mental disorders show a profile that could be defined by the following factors:
Study Limitations
Symptomatology Representation
Study Facilitators
Other Limitations.
The four factors obtained explain 66.763% of the total variance. Thus, we can affirm that the reduction to four factors gives us a satisfactory result.
With this research, we want to gain an in-depth understanding of the way in which mental disorder is represented in the educational environment through the 18 variables used in the population analyzed. We understand that, when implementing educational practices that serve students with severe mental disorders, we should think about working along four different lines. These intervention lines cannot be understood without the others; therefore, the practices to be implemented should involve specific actions in each of the indicated factors.
In this regard, we observe that the factor
Regarding the
The
With the factor
We consider this research as a descriptive approach that brings us closer to the object studied. In statistical terms, the exploratory factor analysis has been statistically relevant, since we obtained a Kaiser-Meyer-Olkin sample adequacy result of .776 and significance in the Bartlett test p < .001. In conceptual terms, we have been able to understand which are the factorial groups that condition the educational reality of a student with a serious mental disorder. These factors indicate the elements to work to help this population achieve academic success. On the other hand, they also point out the main risk factors that a student with a mental disorder could have. This would allow for preventive pedagogical practices. Therefore, we consider factor analysis to be informative for the data set.
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5. Limitations
Finally, we could have investigated in relation to the variables that predict school results in this population. We could also have analyzed the differences between students with externalizing disorders versus internalizing. Likewise, to know with certainty the adequacy of the information from the factor analysis for the data set, it is necessary to perform a confirmatory factor analysis. We are indicated these elements as limitations of the present investigation. We will investigate future research in relation to these elements.
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