Sakari Kainulainen & Reija Paananen
We know that our service system in Finland does not properly meet the needs of young people (Aaltonen et al., 2015; Björklund et al., 2017; Paananen et al., 2019; Blomgren et al., 2020). Young people are offered a wide range of support from the public sector in the fields of social and health care, employment services, and youth work. In addition, support is provided by numerous organizations. Different services for young people are mostly provided by diverse organizations. In addition to the multiple organizations, funding, services, and various professions, there are numerous technologies, personal data records, pieces of legislation, processes, and working methods which are utilized simultaneously. This kind of multi-layered and complicated system has become a problem, especially for young people, and in particular for young people with multiple needs. If we could develop our services and service system according to needs, this could help young people to get the right type of support at the right time, in the right way. This would also save money.
Both research and clinical practice have become increasingly interested in adopting a less stigmatizing approach to young people’s services which would cover the entire life of adolescents (Blomgren et al., 2020). The same authors further note that, achieving better results requires adolescents’ own perceptions and views to be heard. Professionals would benefit from a rapid screening tool capable of assessing different aspects of adolescents’ wellbeing and situations that influence their overall wellbeing. In addition, adolescents would benefit from the support that meets their needs more closely. Notably, adolescents with multiple needs would probably benefit the most, since such a tool would enable young persons to assess their life situation together with professionals and to jointly plan integrated services.
The problem in reconciling young people’s needs and sectorized services described above puts pressure on service systems to change. There is a need for instruments, like 3X10D Survey to make this coordination. Considering people’s needs, especially cases of multiple concurrent needs, requires a human-centric approach to the organization and provision of services. It takes a holistic understanding of the individual customer’s situation as well as (digital) tools to match those needs with existing service offerings. This article describes one possible method of producing a holistic snapshot of the life of a young person and, in particular, evaluates the reliability of the method.
Toward better services for young people
Finland is not alone in the challenges described above. The OECD’s Observatory of Public Sector Innovation (OPSI) seeks to change public administration and service production globally. The customer-oriented construction of services and chains of services is still quite a new concept in public services. According to the OECD (2018), by focusing on the main objectives the public sector wants to achieve, the public sector may tackle crosscutting issues. OECD recommends that governments should 1) focus on a problem, not a method; 2) apply new problem diagnostic tools; 3) analyse potential systemic effects (aims, trade-offs, and real changes); 4) stay open to emergent, bottom-up change; and 5) experiment with transformative change inside government.
At the level of service development, service design is an umbrella concept for myriad different ways to build services to meet customer needs. The most common orientations are user-design and human-centric ways to improve services. The goal of these different formatting methods is that services a) be designed based on user or customer needs rather than the internal needs of an organization; b) be designed through a systems lens rather than as piecemeal components; c) be designed iteratively with the input of users at all stages of development; d) reference a clear purpose, demand for the service, and business case; e) be designed based on data and evidence; and f) be inclusive and accessible (OPSI, 2021).
Development work like the one described above has also been carried out in Finland. In this case, the key issue is to try to identify human needs and connect those needs to the support available. This pairing work can be done traditionally as part of customer encounters, but also using digital tools. The 3X10D survey being evaluated in this article is also used as part of this kind of work in a variety of digital environments and applications. Since there is a broad effort to promote the use of the meter, it is important that it produces reliable information.
In Finland, the human-centric approach is strongly implemented through the AuroraAI programme under Prime Minister Sanna Marin’s government programme. The AuroraAI programme is a broad-based and open stakeholder process implemented through the Public-Private-People partnership (PPP) approach. Systemic change is not possible without cross-sectoral cooperation. The key idea behind systemic change is that
“instead of referral by authorities, citizens can be referred to services by new technologies and the AuroraAI network based on proven uses of services in comparable life circumstances. Artificial intelligence facilitates learning and forking of service chains based on proven uses in the context of specific needs, which will significantly improve the matching of service recommendations and suggested alternatives. In the future, services provided by the public, private and third sectors must be able to respond precisely to citizens’ actual welfare statuses and service needs” (Kopponen & Ruostetsaari, 2019)
How can an assessment of a person’s life situation be obtained such that an artificial intelligence or a professional could make recommendations into services on that basis? One solution is multidimensional self-evaluation via the 3X10D survey. This survey is a tool to describe the life situation of a person to an artificial intelligence system. The 3X10D survey is also the basis for the Zekki self-assessment tool (zekki.fi). Zekki helps young people to reflect on their own wellbeing, gives some guidance for everyday life, and also helps individuals get support in various situations by integrating services and giving direct links to national support associations as well as public services. Information from Zekki is transferred to the AuroraAI-network and is used to find the best services from Public Service Info (Palvelutietovaranto) using artificial intelligence.
This article examines the reliability of the multi-dimensional 3X10D survey using the concept of satisfaction. It is necessary to briefly review the measurement of wellbeing so that it is possible to assess the objectives and relevance of the survey. The key question is whether wellbeing can be measured at all and how the results produced by operationalizing it should be viewed.
The next section reviews the concepts and different metrics of wellbeing relevant to the 3X10D survey.
Different ways to evaluate people’s needs
The 3X10D survey is a quality-of-life mapping metric. Quality of life is a broad concept that can include a wide range of concepts and derived objective or subjective metrics. Objective metrics largely focus on material factors, such as income and level of education. Subjective metrics, on the other hand, give particular weight to a person’s own perception of their objective and subjective wellbeing. Often subjective and objective points are considered at the same time, especially when looking at wellbeing at the community level. For example, Eurostat (2015) describes the quality of life of the population in terms of the following entities: material living conditions (income, consumption, and material conditions); productive or main activity; health; education; leisure and social interactions; economic security and physical safety; governance and basic rights; natural and living environment; as well as overall experience of life.
Subjective wellbeing can be measured by a single question or by multiple questions about different domains of life. These latter, multidimensional wellbeing measurement instruments can be found, for example, at the website of the Australian Centre on Quality of Life. More than 1200 multidimensional metrics mapping subjective wellbeing from different angles have been presented on the site. The site’s founder, Professor Robert A. Cummins, has focused on evaluating changes in wellbeing. According to Cummins, throughout our lives, our wellbeing is at a certain level and wellbeing tends to return to that level if, for some external reason, it is lower or higher than average. According to this model, wellbeing is in homeostasis, in an equilibrium state (Cummins, 2020).
Multidimensional metrics can be interpreted in two different ways: reflective and formative (Diamantopoulos & Winklhofer, 2001). According to the reflective approach, the individual variables involved in the multidimensional metric reflect the latent concept behind single variables. In this case, a group of variables measures one particular phenomenon (like subjective wellbeing) and the variables strongly correlate with each other. According to the formative perspective, the individual variables involved in the multidimensional metric cause the phenomenon covering the variables. Cummins’ Personal Wellbeing Index, for example, is built on this formative perspective. In this case, individual variables correlate with each other, but each individual variable also has its own specific variance.
A narrower way of measuring wellbeing is represented by the view that subjective wellbeing is formed by three factors: life satisfaction, positive affect, and negative affect (Andrews & Withey, 1976; Diener, 1984). Dutch professor Ruut Veenhoven has put together a sizable website of instruments that, he says, describe happiness (Veenhoven, 2021). For him, happiness is almost synonymous with life satisfaction and subjective wellbeing. Happiness is the “subjective enjoyment of one’s life as a whole”. The site has a description of more than 1400 mainly single-item questions on happiness. According to Veenhoven’s theory, life satisfaction is explained as simultaneously the sum of biological needs and culturally formed desires (Kainulainen et al., 2018).
The 3X10D survey as a link between needs and services
The 3X10D survey was constructed in 2015 and 2016 in the City of Kuopio. In that project, a multidisciplinary service structure and multi-professional collaboration were developed. The roots of the 3X10D survey are in a problematic situation in which professionals from several different professions share a common client (Juutinen & Kainulainen, 2017). Traditionally, each profession builds tools from its own theoretical and practical starting points in order to assess the customer’s need for service. This has led to customers needing a wide range of support having to make separate service assessments for each professional. This multiplies the number of services need assessments and causes repetition of customer information handling. Due to varying methods of cooperation, and technical issues in data processing and security in data handling, an overall picture of the client’s life situation is not shared among experts. Therefore, the 3X10D survey was formulated to support the customer’s own view at the general level so that professionals can take advantage of this assessment when defining their own role and support services (Kainulainen & Juutinen, 2017).
The 3X10D survey was originally developed for young people aged 16‒29. The first outlines of the questions were tested in autumn 2015 and discussed in three discussion groups of five young people in high school, vocational college, and a workshop. Questions were reformulated based on the discussions. After the tests, the responsible workers of the project became familiar with the method and its use and developed a systematic and similar type of work with all customers to make it easier for professionals to work (together). During the spring of 2016, data generated by about 80 questions was collected from approximately 900 young people from universities, vocational colleges, workshop activities, and unemployed people, as well as city workers in order to validate the 3X10D survey. First validation results from the data have been reported and the results on the functionality of the survey were promising (Kainulainen, 2019). Correlations between single items of the 3X10D survey are high with gold standard instruments (Kainulainen & Juutinen, 2017). Gold standards are metrics widely used and previously validated in the discipline. The new metric is compared to these, allowing one to assess how good the new survey is compared to the previous ones (de Vet et al., 2011).
Following the development process, the 3X10D survey is a quick, general survey to trace one’s life situation. The basis for the process was a human-centric orientation, so that the life-situation of a client leads the service process (Kainulainen & Juutinen, 2017). The 3X10D survey looks at youth as part of the human life cycle: factors related to the development of a young person, as well as factors related to their life circumstances and interpersonal relationships. The survey includes one question stem: Thinking of the present time, how satisfied are you with… followed by 10 topics such as health, housing, economy, and relationships. Each topic is assessed on a scale from zero to ten. The extremes on the scales are extremely dissatisfied (0) and extremely satisfied (10).
Why is the term life situation used in 3X10D instead of wellbeing? The aim of the 3X10D survey is not (only) to find out a respondent’s subjective wellbeing but to map their view on factors explaining and supporting wellbeing. This viewpoint is in line with the formative approach described above. Question on satisfaction with life as a whole is different. You can see it as describing wellbeing as such. The result of the 3X10D survey shows the respondent’s assessment of their own life situation. The 3X10D survey is epistemologically subjective since, although the data generated is defined by the developer of the survey, it is evaluated by the respondent (see Karisto, 1984, 22–27).
The 3X10D survey has taken advantage of the ideas described above, which are essential to the measurement of human wellbeing, to account for one’s activities in everyday life. First, the survey is based on the idea that a person is the best assessor of their own wellbeing, and the survey produces relevant information about this. Secondly, a multidimensional way of evaluating life is justified, especially when one wishes to interpret the result in conjunction with a professional. From the multidimensional estimate, attention can be focused on the areas of life desired, and again other areas may remain less in focus. To do this, the survey contains key areas of life for all people, but also elements particularly relevant for young people. Areas of life are not meant to merely reflect the underlying concept of wellbeing; these are seen as constitutive of the subjective wellbeing of a young person. The 3X10D survey is thus a formative wellbeing measure. The items of the 3X10D survey are semi-abstract and refer to the self proximally, rather than distally as in the PWI metric (see Cummins et al., 2003, 164–165). Third, satisfaction reflects emotions and the level of achievement relative to one’s stated goals. As a result, measuring satisfaction (in different domains) provides a good foundation for young person to orient his or her future. The 3X10D survey is a generic measure of the life situation of a person.
The 3X10D survey has been utilized in many different contexts and is being deployed in many different organizations. In spring 2021, the 3X10D survey is either used or is going to be used in the near future in several digital programmes giving young peoples’ perspective to services. For example, school nurses can use the digital tool in their daily work with school children via the national omaolo.fi platform. Soon, an extra module will be added to omaolo.fi which will help professionals working with young people placed outside their home. In spring 2021, the 3X10D survey was launched nationally via the Zekki platform (Paananen et al., 2021) and further developed for young people living in the Southeast of Finland (Kymenlaakso and Etelä-Karjala) (Gronvall, 2021). Studies have dealt with young people at risk of exclusion; schoolchildren (in four countries in Europe); people with epilepsy (Blomgren et al., 2020; Hakala et al., 2019; Häme & Häme, 2021; Kettunen et al., 2021); and the functionality and exploitability of the survey in different situations (Väänänen, 2017; Huotari, 2020).
Aim of the article
The validity of the 3X10D survey has been evaluated earlier and face, content, criterion, and concurrent validity all seem to be sufficient (Kainulainen, 2019). According to evaluation by TOIMIA-professionals (TOIMIA-database, n.d.), the 3X10D survey appears to be a promising metric based on initial user experience, but there is only limited research data on this. It is estimated that the metric is particularly suitable for counselling work with young people, where there is a need to gain an overall understanding of the young person’s life situation.
The functionality of the 3X10D survey has also been assessed as part of the national recommendations for the rehabilitation of young people as one of the five recommended metrics. The purpose of the recommendation was to help professionals working with NEET youth (Not in Employment, Education, or Training) to identify the impaired functional capacity of NEET youth so that young people can be guided into the professional rehabilitation provided by Kela. According to Sandberg et al. (2018), it may be possible using assessment tools to identify at a general level what aspect of life young person is struggling with, but describing the problem more accurately requires (face-to-face) discussion with a young person. The working group did not recommend the development of a new assessment tool for that purpose. In an organization, the use of an assessment tool (such as 3X10D survey) must be a considered part of the customer process, aiming to bolster the customer’s own strengths and inclusion (Sandberg et al., 2018).
Our basic aim in this article is to test some psychometric properties of the 3X10D survey. The psychometric properties of metrics refer to information about their validity, reliability, and responsiveness. According to a division established by the International Group of Cosmin researchers (de Vet al., 2011, 4), the psychometric properties of metrics can be classified using four criteria: reliability, validity, responsiveness, and interpretability. The aim here is to analyse the reliability of the 3X10D survey. In practice, this means deciding whether we can trust the results of survey. We have two hypotheses based on earlier studies and knowledge. According to our experiences, 1) life situation is mostly stable and therefore self-assessment of it stays mostly at the same level within a short period of time; and 2) people can make a difference between acute situations and life situations on average.
Data and methods
We had three different types of questions in a questionnaire. Firstly, there were questions from the 3X10D survey; secondly, there were questions addressing the acute situation of the respondent; and thirdly, there were background questions capturing details of personal characteristics and academic studies.
At the beginning of the 3X10D Survey, instructions for responding in the following manner were presented: First read the question and then evaluate the topic from each line and tick what you think is the most appropriate number (0–10). Zero means you are extremely unsatisfied and ten extremely satisfied. At number five, you are neither dissatisfied nor satisfied. This main question was followed by the instruction: Thinking of the present time, how satisfied are you with…
1) your status of health,
2) your abilities to win forthcoming challenges,
3) your housing,
4) your managing in daily activities (for example studying, working),
5) your family(-members),
6) number of your trusted friends,
7) your financial situation,
8) developing your strengths (for example by pleasant hobby),
9) your self-esteem,
10) your life as a whole.
A key objective was to assess how well the 3X10D survey can describe a general situation, i.e., a slightly wider period than the very moment of response. To clarify this, questions surveying the acute situation were added to the survey. This would allow one to find out to what degree the response moment activity affects the more general assessment of life situation. These questions chart where the respondent has recently been, with whom, and doing what (Table 1). The mobile tool also collected the respondents’ gender, age, type of degree, and academic career.
Table 1. Questions describing social situation before response to the 3X10D Survey.
|“Where were you when answering?”||At home / Campus or working place (etc.) / In the city (downtown) / Outdoor, in the nature / Visiting friends (etc.)|
|“Whom were you with?”||Alone / With children / With coworkers (etc.) / With friends (etc.) / With spouse (etc.)|
|“What were you doing?”||Doing business (in office etc.) / Eating, lunch or dinner / Everyday routines / Sleep, rest, read, music, TV / Social, Dating, Exercising / Studying / Taking care of children / Working.|
An important part of the assessment of psychometric properties is the reliability of the metric (Valkeinen et al., 2014). Same authors describe in a detailed way how to measure reliability concretely. According to them, reliability indicates the extent to which a measurement or research method used reliably and reproducibly measures the desired phenomenon. In repetition measurements, the investigation dates should not be so close to each other that the respondent readily remembers the answers from the previous time, but also not so remote that environmental factors affect the result. A good length of time has been held to be about two weeks. Repeatability can be defined as a measure describing the variation of repeated measurements performed under the same conditions. Reproducibility is again an indicative of the variation of repeated measurements carried out under different conditions. Good reproducibility under the same conditions is an important prerequisite for reproducibility under different conditions. For the purposes of persistence of phenomenon, the term stability is used (Valkeinen et al., 2014).
Giavarina (2015) and Valkeinen et al. (2014) look at two ways to make repeatability measurement. Repeatability is studied by the concurrence of repetition measurements. Repeatability can be viewed as relative or absolute. The former considers the positions of the measurables relative to each other between different measurements. The latter, on the other hand, compares absolute values between measurement times. A review with absolute values is well suited to comparing repetition measurements. The standard error of the measurement, limits of agreement, and coefficient of variation are most often used to calculate repeatability.
Many times, the repeatability of a measure is evaluated using a correlation coefficient, but this does not produce reliable information on the measure’s agreement between different measurement times or metrics. In their seminal article, Martin Bland and Douglas Altman (1986) introduced a better way to evaluate the repeatability of a metric. The Bland-Altman drawing is a graphical method used to assess the agreement of two methods or measurement times. In the Bland-Altman method, values are reported directly, and systematic differences between methods or measurement times can be seen from the figure.
It is possible to assess the reliability of the measure by longitudinal examination and by comparing the persistence of the result in time. In this case, it should be possible to distinguish the situational effects from variance. Considering these situational effects seems to give a more realistic result of the reliability of a measure (Lucas & Donnellan, 2012). In this pilot study, we gathered (partly longitudinal) data with some additional information telling us where the respondents were just before answering, with whom they were, and what were they doing (640 evaluations). To test the first hypothesis, “life situation is mostly stable and therefore self-assessment of it stays mostly at the same level within a short period of time”, we compared the satisfaction of the life domains at time-point one (T1) and time-point two (T2). In addition, we analysed the differences between the assessments to measure the reliability of the 3X10D survey. To test the second hypothesis, “the acute activities are not affecting the assessments of life situation in general”, we compared the satisfaction of the different life domains in different everyday situations. To test the hypotheses, satisfaction of different domains were tested in different time-points and situations with General Linear Modell Univariate and F-test. According to SPSS 27 Manual, the GLM Univariate procedure provides analysis of variance for one dependent variable by one or more variables. Using this General Linear Model procedure, it is possible to test null hypotheses about the effects of other variables on the means of various groupings of a single dependent variable.
The data was mainly analysed by describing students’ life situation satisfaction at different times of answering and comparing the answers with those in different social situations. The F-test was used to test for differences in the means between groups in general, and the Kruskall-Wallis test was used to compare differences between individual categories within the groups. To test repeatability, Bland-Altman indicators were calculated, and figures are presented for each variable in 3X10D survey based on the first and second tests (N=105) below. In addition, the coefficients of variation (CV) for the corresponding variables were calculated, as well as the intraclass correlation coefficients (ICC) and standard error of measurements (SEM) for every variable.
These measures do not determine exactly whether the items are reliable or not, but they give advice to make a decision on the reliability. CVs should be as small as possible, ICC should be as close to one as possible (>.9 is excellent, .7–.9 is moderate, <.7 is poor), and SEM as small as possible. The coefficient of variation were calculated with the formula “CV=StD / Mean”. Intraclass correlation coefficients (ICC) were calculated with reliability analysis (ANOVA). The standard error of measurement coefficients were calculated with the formula “SEM=StD * Sqrt (1-ICC)” (de Vet et al., 2011). All statistical analysis was carried out on the SPSS 27 programme.
The data was collected in autumn 2018 to test a mobile version of 3X10D. First, an email was sent on 17.9.2018 to 2 590 students of one university. It was known that most of the students do not use the email they were given by the university. Unfortunately, this email was the only way to try to reach all the students. Another challenge was that the students had to give their phone number to get a link to the survey. A reason for this was that we wanted to follow the life situations of students during the autumn. During September, 73 students gave their responses, half of them only once, and the other half twice or more. Two weeks later (30.9.), the first reminder was sent to all students, and then once a month after that until the end of the year. During October, 127 students answered, and, during November, 93. In the last reminder 10.12.2018, we added the possibility to complete a self-assessment anonymously, without giving the phone number. In the last month, 159 students answered, and 9 after that. This means that a total of 462 students were involved, and 105 of them more than once. The response rate was 18 per cent. The background of the students is shown in table 2.
Table 2. Gender, age, and academic year of respondents (N=462).
|Not specified||4||0.8 %|
|Age group||18–24||133||28.8 %|
|Academic year||First||132||28.6 %|
|Fourth or more||68||14.7 %|
|Not specified||5||1.1 %|
With the collected material, it is possible to look at reliability in two ways. First, the effects of the social situation and activities of the moment of response can be analysed. In this case, variation is analysed based on all assessments made (N=640). Secondly, one can evaluate how similar or different the life situation assessment remains over time. In the vast majority of cases, the retest was completed either the following month or the same month as the first test. In this case, only those respondents who responded to the survey twice or more often (N=105) were analysed. We are very aware of the specific homogeneity of the material consisting of students’ responses. Students’ wellbeing differs, for example, from the wellbeing of unemployed youth. However, the key goal was to examine the changes in the self-assessments at different time-points, rather than differences in the wellbeing of the groups. Therefore, we consider the material suitable for the study.
In this section, we consider the reliability of the survey in the light of the material. First, we describe the average values of different items of the 3X10D survey and compare the results by manner of participation and those involved in the study in different ways. After that, we look at how consistent were the first and second responses of the same respondents. Third, we analyse the possible impact of social situations on responses.
Respondents’ satisfaction with family members (M 8.15; StD 2.06) and housing (M 7.94; StD 1.91) were highest, while financial situation (M 5.8; StD 2.42), developing one’s own strengths (M 6.47; StD 2.08), and self-esteem (M 6.56; StD 2.11) were lowest (Table 3). The profiles of satisfaction with domains of the 3X10D survey were very similar in different groups of respondents. The response profiles also generally corresponded to the profile of the previously compiled validation data (Kainulainen & Juutinen, 2017). Satisfaction levels in this study were almost the same for health, resilience, housing, coping with everyday life, family, and friends. On the other hand, levels of satisfaction were slightly lower in terms of economy, self-esteem, life satisfaction, and especially self-development (Kainulainen & Juutinen, 2017).
Among respondents, there were those who completed the self-evaluation only once and those who completed it two or more times. These different subgroups give us the possibility to analyse whether there were differences (self-selectivity) that should be considered when interpreting the results. Responding to surveys is always optional, and, as a result, particularly in follow-up surveys, there is a risk of selective occurrence such that people with different attitudes or life situations are kept in the survey. The data, then, is split into different subgroups, as shown in Table 3. There is no statistically significant difference analysed with F-test in satisfaction with domains of life between those students who answered only once (n=351–356) and those who answered twice or more times (n=103–106). Nor were there statistically significant differences when comparing the responses at time points 1 and 2 (n=103–106). It is also obvious that no big changes can be recognized from the first self-assessment to second (n=103–106), third (n=45) or later (n=26) assessments. The level of satisfaction differs from domain to domain but stays quite stable during the waves. The same result, stable satisfaction, can be seen as well from Bland-Altman figures (Figure 1). Bland-Altman is used even though the correlation coefficients between the first and second times are relatively strong, ranging from .53 to .78., because the coherence of measurements cannot be inferred from correlations. The correlations matched previous results on the persistence of students’ happiness over a four-week period (Brunstein, 1993). A similar correlation (.50) in life satisfaction has also been observed in the adult population, although the follow-up period was as long as one year (Ehrhardt, J. et al. 2000).
Table 3. Satisfaction with items of the 3X10D survey according to number of responses (one response versus the mean average of two or more).
|Item of the 3X10D survey||One response (n=354)|
|First (n=105)||Second (n=105)||Thirdth (n=45)||Fourth or more (n=26)||Mean||StD|
|Status of health||7.28||7.28||7.06||7.11||7.31||7.24||1.82|
|Abilities to win forthcoming challenges||7.33||7.35||7.20||7.36||7.77||7.33||1.83|
|Managing in daily activities (for example studying, working)||7.42||7.46||7.10||7.31||7.58||7.37||1.89|
|Developing your strengths (for example by pleasant hobby)||6.43||6.42||6.59||6.70||6.39||6.47||2.08|
|Life as a whole||7.39||7.41||7.09||7.36||7.58||7.34||1.74|
Two notable findings rise in the review with Bland-Altman figures (Figure 1). First, you can see from figures that there is no big difference between the first and second measurement. This can be seen from the fact that the differences in averages are very small – near zero – for all items of the 3X10D survey. This means concretely that observations remain within limits of agreement (LOA) boundaries (dotted lines). Another finding is that the differences are relatively similar at all levels of the means.
The vast majority of observations remain within 95% of limits of agreement (LOA), although, in each review, a small percentage of observations differed between the first and second measurement times more than that limit. This dispersion was also observed in the coefficients of variation (CV), which indicates the dispersion of measurement times relative to the mean (as a percentage). Variation is at its lowest in terms of housing (CV 19%), and at its highest in terms of economic situation (CV 40%). For friends, self-improvement, and self-esteem, the amount of variation varies from 27% to 30%. For other factors, the variation is about 24%. Intra-correlation coefficients (ICC) varied from 0.69 (resilience) to 0.88 (friends). This means that reliability was mostly at a sufficient level (>.7), and, in two items (friends, finance), close to an excellent level. The standard error of measurement (SEM) varied from 0.69 (housing) and 0.73 (friends, life) to 1.0 (hobby). The remaining items varied within these limits (0.88–0.94).
The most variation seems to be in the middle of the response scale or below that (scale from 0 to 5) in the averages. This result may suggest that, at halfway through the scale the respondent has not been dissatisfied or satisfied with the matter in question. Therefore, more variation exists between the response times when respondent did not have a clear assessment at the first assessment. Conversely, respondents who had a clear assessment of their life situation at the first time of measurement had a clear understanding of the second time as well.
In general, the effects of transient life events on enduring satisfaction levels over time are mild across all domains of life. No statistical differences can be seen (Table 4) between those respondents’ locations within the last hour before answering the survey, but some differences in satisfaction are visible related to social life and activities. The assessment of resiliency was related to whom respondent was with and what he or she was doing before answering. The highest level of satisfaction with participants’ abilities to win forthcoming challenges was among those persons who were surrounded by other people, especially by their own children or friends. In contrast, satisfaction was lowest among those students who were socially isolated. People who maybe have little contact with other people feel that their resiliency is weaker than other people’s too. Also, those students who were actively doing something with other people felt that their resiliency was better than those who were (passively) resting, listening to music, or watching TV, etc. Satisfaction with developing one’s own strengths by doing interesting things (e.g., hobbies) was also related to what a person was doing before answering. Doing things with another person (dating, social life, exercising) correlated with a higher level of developing one’s own strengths, while doing business or taking care of children correlated with lower levels.
The action at the time of response does not seem to reflect heavily in participants’ assessments of their general life situation (Table 4). The respondent can distinguish between the broader perspective of the 3X10D survey, and actual social activities. With whom, where, and what doing before responding does not strongly affect the assessment of the life situation. For where the respondent had been, there was no statistical association with satisfaction with any area of life. The ability to get out of trouble in life was affected by whom the respondent had been with before the moment of response (P<.04) and for what the defendant had been doing was a statistical link with coping with difficulties (P<.02) and self-improvement (P<.04). According to previous studies on well-being, social relationships have a strong impact on life satisfaction. This relationship was also seen in our analysis: those who were alone at the time of the response reported at a lower level of life satisfaction than other respondents.
Table 4. Means of the 3X10D survey by place, social contacts and type of activities done before the survey (F-test, N=639).
|Status of health||Abilities to win||Housing||Managing in daily activities||Family(-members)||Number of your trusted friends||Financial situation||Developing your strengths||Self-esteem||Life as a whole|
|Where were you?|
|At home (366–371)||7.11||7.23||8.02||7.35||8.15||7.50||5.77||6.47||6.58||7.30|
|Campus or working place (etc.) (171–175)||7.30||7.37||7.66||7.29||8.04||7.55||5.78||6.52||6.59||7.30|
|In the city (downtown) (45)||7.73||7.82||8.33||7.51||8.42||7.36||6.67||6.13||6.58||7.73|
|Outdoor, nature & hobbying (28)||7.79||7.86||8.14||7.89||8.29||8.04||5.04||7.14||6.57||7.79|
|Visiting friends (etc.) (19)||7.05||6.84||7.74||7.26||8.16||8.58||5.39||5.74||5.95||6.89|
|With children (98)||7.24||7.78||7.97||7.54||8.26||7.14||5.64||6.46||6.74||7.58|
|With coworkers (etc.) (121–124)||7.36||7.40||7.72||7.39||8.21||7.61||5.76||6.48||6.63||7.35|
|With friends (etc.) (49)||7.55||7.55||8.06||7.55||8.00||7.81||6.22||6.42||6.57||7.33|
|With spouse (etc.) (97–99)||7.12||7.29||7.94||7.75||8.66||7.77||5.76||6.69||6.95||7.68|
|What did you do?|
|Doing business (in office etc.) (62)||7.15||7.45||7.58||7.08||7.89||7.00||5.57||5.97||6.44||7.25|
|Eating, lunch or dinner (38)||7.13||7.37||8.32||7.34||8.00||7.82||6.08||6.74||6.53||7.21|
|Everyday routines (121–123)||7.39||7.49||8.21||7.67||8.31||7.63||5.71||6.56||6.49||7.38|
|Sleep, rest, read, music, TV (125)||6.77||6.82||7.87||7.04||7.80||7.54||5.52||6.28||6.27||6.96|
|Social, dating, excersising (32–34)||7.82||8.00||8.47||7.74||8.50||8.33||6.41||7.15||7.00||7.85|
|Taking care of children (19)||7.26||7.42||8.05||7.05||8.63||6.26||5.05||5.56||6.00||7.26|
Some statistical difference between the students of English and Finnish study programmes were recognized. Satisfaction with family members (7.08 vs. 8.19; p<0.01) and trusted friends (5.92 vs. 7.63; p<0.000) was lower in English than in Finnish study programmes. These results are understandable; students coming from abroad have weaker social ties than students who have lived in a country for a long time.
Phase of study also slightly affected two domains of life: firstly, self-reported health was lower in the first and second year, and highest in the fourth (or later) study year (p<0.01). Secondly, self-esteem was highest in the third study year, but there was no gradual increase or decrease. Therefore, it is not possible to explain why the third year was a bit exceptional.
The 3X10D survey was developed to support multi-professional and multidisciplinary work with young people. The survey is intended to strengthen the young person’s role in the service process and make it easier for professionals to work together, particularly in supporting young people who need a lot of support. The survey is also one possible measure to use with other purposes, like in population surveys to show how people see their life as a whole.
The survey has been provisionally identified as a useful and valid tool in concrete face-to-face customer work. Besides, the 3X10D survey produces statistical information which can be used in statistical analysis. However, there had been no detailed data on the reliability of 3X10D survey until he present work. Our aim was to analyse whether the 3X10D survey is a reliable method to describe a person’s life situation in general. We had two hypotheses: firstly, life situation is normally quite stable, and, therefore, satisfaction with different spheres of life stays mostly at the same level over short spans of time; and, secondly, people can distinguish between their acute situation and their situation on average.
Both hypotheses received support from the empirical evidence. In Table 3, we showed the mean average satisfaction levels in different domains of life at different times of self-assessment. The results showed that no remarkable changes in life situation were visible during the period we examined. Satisfaction levels in some domains increased slightly, but, in other domains, satisfaction decreased or did not change at all. The reasons for these small changes might include changes in a student’s life situation, or they could be inherent in the survey instrument or our analysis. Due to the constraints of the data, small variations in the results between the first and second measurements cannot be verified. We do not have enough other information about possible changes in people’s lives between measurement times. Individual participants may have found a friend, got a part-time job, or something positive or negative may have happened in their life that is reflected in the results. This issue can be evaluated to some extent by comparing information other than the 3X10D survey in the data (with whom, where and doing what) using different response waves. However, it seems that, among the respondents who responded at least twice, there are no major differences between the early and late responses in respect of their social situation. They had, at both times, responded most frequently at home, and had been doing their usual chores. Slightly more often, the respondents were in the company of a spouse or friends on the first occasion, but alone on the second. Consequently, a more detailed analysis of the repeatability and reproducibility of the 3X10D survey remains for future research.
The response profiles for college students were very similar to those found in previous analyses. In other words, young adults are happy with some things and dissatisfied with others. Family, housing, friends, and life in general are often quite satisfactory. In contrast, economics and self-esteem are aspects of life with which respondents are more often discontented than with other aspects. Comparing with material collected a few years earlier, the profiles are almost identical (Kainulainen & Juutinen, 2017). Averages differ by only 0.2 points on average (scale 0-10), with the greatest differences in strength development, economy, and self-esteem. This basic agreement between two different data sets suggests that a group of respondents consisting of students can also be considered representative of young people more broadly.
Our second hypothesis also received support from the data. Self-assessment was not strongly influenced by recent activities in students’ lives. In addition, satisfaction with the various domains of life was similar irrespective where the students were just before their made their self-assessments. A similar result also came to Jayawickreme et al. (2017) when looking at the connection of emotional states to overall life satisfaction. Changes in instantaneous emotional states do not predict changes in life satisfaction.
One sphere of life, ability to win forthcoming challenges (resilience), was influenced by students’ social lives: satisfaction with resilience was higher among those students who were surrounded by people than among those who were alone. This social aspect was also seen in what people were doing: satisfaction with resilience was the highest among those students who were dating etc. before answering. This kind of activity was also linked with higher satisfaction with developing one’s strengths.
The profile of young people’s satisfaction with different spheres of life is quite the similar in different subpopulations. This profile recurs in the general population as well as in the pilot population. Only one exception can be seen: in the general population, young people are more satisfied with their possibilities to develop their strengths than in the pilot population. This profile remains the same in every subpopulation of the pilot group, irrespective of how many times they responded.
Based on the results in this article and an earlier one (Kainulainen & Juutinen, 2017), we conclude that the 3X10D is a reliable tool for evaluating life situation. Changes in the results of the person’s self-evaluation should be evaluated as changes in the person’s wellbeing. Thus, the 3X10D survey can be used in determining the outcomes and impacts of activities on wellbeing.
Adjunct professor (wellbeing research) Sakari Kainulainen works as a RDI-specialist in Diaconia University of Applied Sciences.
Adjunct professor (social medicine) Reija Paananen works as a project manager of Digital support for young people – 3X10D project in Diaconia University of Applied Sciences.
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