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What makes a person self-sufficient?

Self-sufficiency is defined as the ability of individuals to attain an acceptable level of functioning regarding specific life-domains, such as daytime activities and social support.

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Study design

The Medical Ethics Committee of the Erasmus University Medical Center Rotterdam reviewed the research proposal and declared that the Dutch Medical Research Involving Human Subjects Act (Dutch abbreviation: WMO) did not apply. They issued a declaration of no objection to conducting this study and permitted to submit the results for publication in a scientific journal in the future (proposal number MEC-2015-614). All participants provided written informed consent.

Setting and study population

A total of 22 intermediate vocational education school locations were invited to participate in the study (Fig. 1). Twelve locations could not participate, mainly because of the anticipated time investment. Finally, ten schools participated in the study in the Dutch regions of Utrecht, West-Brabant, Amsterdam, and Rotterdam. The baseline data was collected between December 2015 and October 2016. The follow-up data were collected between July 2016 and April 2017.

Fig. 1 Flowchart of the participants in this study Full size image

Participants were students aged 16–26 years attending intermediate vocational education level 1–4 (i.e. level 1 is considered assistant training and level 4 is considered middle management training). They attended the following vocational programs: media manager/developer, assistant care, trade, interior design, nursing, and technician studies. To adhere to the preferences of the schools, two different procedures were followed to select participants. (1) At eight schools, a school employee selected and invited students for participation if they had reported an extensive amount of sick days from school (i.e. reporting sick at least four times, or, more than six consecutive school days in twelve school weeks). (2). At the two remaining schools, all students in randomly selected classes were invited to participate. The involved researcher selected students who met the criteria for extensive sickness absence afterward. All students from the first procedure and students from the second procedure who met the criteria for extensive sickness absence were placed in the ‘selected sickness absence’ sample. The remaining participants from the second procedure were placed in the ‘general school population’ sample (see Fig. 1). All students and parents of students between 16 and 18 years of age were informed about the study through an information letter and a leaflet. These documents explained the aim of the study and included contact information of the researchers. An appointed coordinator from the participating schools sent the documents to the students and parents. Parents could object to having their child participate in the study by notifying their objection towards school or the researchers. The students were asked to provide written informed consent before filling out the questionnaire. The appointed coordinator collected all completed questionnaires and sent them to the researchers. Approximately 6 months later, the participants in the selected sickness absence sample (i.e. where the selection was based on the amount of sickness absence) received the follow-up questionnaire at home with a return envelope or through a link in their e-mail. The broader school sample was not invited to fill out the follow-up questionnaire. In total, 758 students provided written informed consent at baseline. Before analyses, we excluded students who were older than 26 years (n = 3).

Measurements

The questionnaire contained the following topics: self-sufficiency, contextual factors (i.e. socio-demographic characteristics and context, and risk behaviors), and indicators of health status (i.e. sickness absence and depressive symptoms).

Self-sufficiency

An adapted version of the Dutch self-sufficiency matrix (SSM-D) was included in the questionnaire [1, 21]. This version was developed for and validated among students attending intermediate vocational education, corresponding to the language skills of the students, and addresses the ability of students to provide for themselves regarding 11 specific life-domains (finances, daytime activities, housing, domestic relations, mental health, physical health, addiction, activities daily life, social network, community participation, and judicial) [21]. Each life-domain was assessed by how many problems the student had in the past 6 months in a certain life-domain, e.g. ‘Finances. Think of: having the money to make ends meet’ (Additional file 1: Table A1). Five response categories ranged from ‘no problems’ to ‘many problems’. Response categories were dichotomized into ‘self-sufficient’ (few problems and no problems) versus ‘not to barely self-sufficient’ (many problems to not few/not many problems) for each life-domain. For analyses purposes, one overall self-sufficiency score was calculated, ranging from self-sufficient on all life-domains, ten life-domains, nine life-domains, and eight or fewer life-domains.

Contextual factors and indicators of health status

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Elements of the International Classification of Functioning, Disability, and Health (ICF) from the World Health Organization were used to select relevant factors [22, 23]. In this framework, human functioning is considered at the level of the whole person in a social context. We applied an adapted version of this framework to our study (Additional file 2: Figure B1).

Contextual factors

Socio-demographics and context included age (years), gender (boy/girl), level of intermediate vocational education (higher level 4 vs. lower levels 1–3), ethnic background (Dutch versus non-Dutch by following the definition of Statistics Netherlands [24]), living situation, and perceived school performance. For living situation, ten different response categories were dichotomized into ‘living with a caretaker’ versus ‘living without a caretaker’. Perceived school performance was assessed by the question: “How do you think your teacher estimates your school performance compared to your classmates?”. Five response categories ranged from ‘very good’ to ‘not good’, and were dichotomized into ‘good’ versus ‘average or less’. Previous research showed this item can distinguish students who get good grades at school from students that do not [25]. Risk behaviors included cigarette smoking, binge drinking, cannabis use, delinquency, and truancy. Cigarette smoking was assessed by the question: “How often do you currently smoke?”. Four response categories ranged from ‘not’ to ‘yes, daily’ and were dichotomized into ‘current smoking’ versus ‘no current smoking’. Binge drinking was assessed by the question: “How many times did you consume five or more alcoholic drinks on one occasion in the past four weeks?”, by following the international definition of binge drinking [26]. Seven response categories ranged from ‘never’ to ‘nine or more times’ and were dichotomized into ‘not once’ versus ‘one or more times’. Cannabis use was assessed by the number of times the student had used cannabis over the past 4 weeks. Eight response categories ranged from ‘never’ to ‘20 or more times’ and were dichotomized into ‘not once’ versus ‘one or more times’. Criminal behavior was assessed by ten items covering criminal behavior in the past 6 months (e.g. bought stolen goods). Ten response categories ranged from ‘never’ to ‘six times’ or more. All ten items were merged into one dichotomous item ‘no criminal behavior’ versus ‘at least one criminal behavior’ in the past 6 months. Truancy was assessed by the question: “Have you been truanting in the past four weeks?”. Six response categories ranged from ‘no truancy’ to ‘more than 20 hours of truancy’, and were categorized into ‘never’, ‘1–5 h’ and ‘more than 5 hours’ [27].

Indicators of health status

Sickness absence was assessed by the question: “How many days in the past eight school weeks did you stay home from school, because you were sick? (do not count holidays)” [27]. The continuous score for number of sick days was used. Depressive symptoms were assessed using the validated Center for Epidemiologic Studies Depression scale (CES-D) [28, 29]. The CES-D is a 20-item scale used to determine the clinical relevance of depression. The items cover the main components of depressive symptoms such as depressed mood, guilt, feelings of helplessness, loss of appetite, and sleep. The frequency of experiencing these symptoms in the past week was assessed. Four response categories ranged from ‘always’ to ‘hardly ever’. The continuous total CES-D score was used with higher scores indicating higher levels of depressive symptoms (range of 0–60).

Data analyses

Descriptive statistics were used to describe the socio-demographic characteristics of the study population (Table 1). We compared participants who completed both the baseline and follow-up questionnaire with participants lost to follow-up using chi-square tests (for categorical variables) and independent sample t-tests (for continuous variables) (Additional file 3: Table C1). Also, descriptive statistics were used to show the distribution of students who were self-sufficient and who were not self-sufficient in each life-domain at baseline and 6 months post-baseline (Table 2). Table 1 Socio-demographic characteristics of the study population at baseline (N = 755) Full size table

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Table 2 Baseline and 6-months follow-up distribution of overall self-sufficiency and of the separate self-sufficiency life-domains Full size table We examined associations of contextual factors and indicators of health status (i.e. predictor variables) with self-sufficiency (i.e. outcome variable) using ordinal and logistic regression analyses. First, ordinal regression analyses were performed on baseline data to analyze the association between predictor variables and overall self-sufficiency at baseline as the ordinal outcome variable (ranging from self-sufficient on: all life-domains, ten life-domains, nine life-domains, and eight or fewer life-domains) (Table 3). Second, logistic regression analyses were performed on baseline data to assess the association between predictor variables and the separate self-sufficiency life-domains as outcome variables (Tables 4). Third, ordinal regression analyses were performed to analyze the association between baseline predictor variables and overall self-sufficiency at follow-up as the ordinal outcome variable (ranging from self-sufficient on: all life-domains, ten life-domains, nine life-domains, and eight or fewer life-domains) (Table 5). We also explored interaction between gender and all other predictors in the association between baseline predictors and baseline overall self-sufficiency. No significant interaction was found. Table 3 Results of the cross-sectional ordinal regression analyses evaluating associations of contextual factors and indicators of health status with overall self-sufficiency (n = 755) Full size table Table 4 Results of the logistic regression analyses evaluating associations of contextual factors and indicators of health status with separate self-sufficiency life-domains (n=755) Full size table Table 5 Results of the longitudinal ordinal regression analyses evaluating associations of contextual factors and indicators of health status with overall self-sufficiency (n = 200) Full size table Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. For the ordinal regression models, we present the univariable and multivariable models. The estimated ORs represent the odds for a student to be allocated within a higher self-sufficiency category if they would have scored one point higher on a predictor variable [30]. We considered a p-value of 0.05 or lower to be statistically significant. All analyses were performed using SPSS version 25 (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.).

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