Health Education Research, Vol. 14, No. 3, 339-355,
June 1999
© 1999 Oxford University Press
Health-related lifestyle in adolescence—origin of social class differences in health?
Department of Public Health, University of Turku, Lemminkäisenkatu 1, 20520 Turku,
1 Tampere School of Public Health, University of Tampere, Box 607,33101 Tampere and Tampere University Hospital, Box 2000, 33521, Tampere, and
2 National Research and Development Centre for Welfare and Health (STAKES), Box 220, 00531 Helsinki, Finland
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
Survey data collected by mail, representing Finnish 16 year olds (N = 2977; response rate 83%), were used to identify which particular aspects of lifestyle are typical of adolescents who select various educational tracks and, thus, have different probabilities of ending up in low or high social positions. The dependent variable, educational track, was formed by classifying the respondents into five successive categories predicting their social position in adulthood. Lifestyle is measured by health behaviours, leisure-time activities and social relations. The probability of belonging to educational tracks with good social prospects in adulthood was high among adolescents who placed much emphasis on health-enhancing behaviours (not smoking, physical exercise, low milk-fat diet, dental hygiene, use of seatbelts, etc.), who did not spend much time watching TV or listening to music and who attended church or other religious meetings weekly. Health-related lifestyle, at the age of 16, is oriented towards the social group the individual is likely to belong to as an adult. The study provides evidence for a strong association between health-related lifestyle and educational track in adolescence.
|
Introduction |
|---|
|
TopAbstractIntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
A way of life involving risks for ill-health is in general more common among people in lower social classes or those with a poorer education. These behaviours include, for example, smoking, excessive alcohol consumption, lack of physical activity, bad sleeping patterns, unfavourable food habits (e.g. a high fat intake), coffee drinking, and engaging in high-risk activities and sexual practices (Macintyre, 1986
; Hasan, 1989; Marmot et al., 1991; Adler et al., 1993; Glendinning et al., 1994; Mackenbach, 1994; Helakorpi et al., 1995; Pill et al., 1995). Since these behaviours are associated with both health and social class, they may be hypothesized as being factors producing socioeconomic health differences.
The interaction between social class and health-related lifestyle is apparent already during the early years of life. The social environment of children and adolescents, consisting of, for example, family and peers, constitutes the context in which behaviours are learned, encouraged and practised (Taylor and Repetti, 1997). Some health-related behaviours can be seen as consequences of various styles of coping with stress, caused by the social environment. Inappropriate behaviours come from dysfunctional coping styles, like helplessness or hopelessness, altered perceptions of risk and vulnerability, and an undue willingness to behave in risky ways. Among the factors that form people's resources for confronting the stressors are socioeconomic conditions, the type and severity of life events, the amount of social support, and the access to information. These all differ according to social class, although there are also individual differences in responding to influences coming from the environment (Rutter and Quine, 1994; Taylor and Repetti, 1997). On the other hand, the interaction between social class and health-related lifestyle may arise from the economic possibilities and other contextual factors, e.g. wholesome food may be more expensive, which limits the actual possibilities of the poor to maintain a healthy diet. Also, in households which do not have a car, it is difficult to take children to instructive hobbies, like many forms of exercise and sports.
It is possible that the same features of social environment which constitute the association between social class and health-related lifestyle influence the association between social background and education, as well. Acquiring education may be an indication of a person's feeling that life is under control to the extent that far-reaching plans can be made. The realization of their plans requires a strong self-esteem and is hardly possible without support from one's immediate circle. It can be thought that giving value to knowledge is reflected in the appreciation of education as well as of health-promoting behaviours.
Many studies show that children from higher social class families reach the highest levels of education (Cobalti, 1990; Blackburn and Marsh, 1991; Mehan, 1992; Roberts and Parsell, 1992; Mauger, 1993; Erikson and Jonsson, 1996; Bourdieu and Passeron, 1977). Economic resources influence the costs attached to decisions about whether or not to continue schooling. Also the choice between academic and vocational studies is affected by the costs, benefits and probability of success. However, background factors other than economic resources seem to be more important for explaining the association between social origin and educational decisions (Erikson and Jonsson, 1996).
Social class differences in children's home environments, like patterns of interaction between parents and children, may explain why academic performance is better among children from higher social classes. More highly educated parents place a high value on education and may help their offspring's educational performance, e.g. by verbal training and practical help with schoolwork. (Argyle, 1994; Erikson and Jonsson, 1996). They transmit to their children cultural capital that helps them to adapt to school values and to navigate the educational system (Bourdieu and Passeron, 1977). Well-educated parents also tend to have a great confidence in their children's probability of success and in their own capacity to get them through the more demanding tracks. Lower class parents need stronger evidence of their children's potential before making decisions about higher education (Erikson and Jonsson, 1996).
Altogether, it is hypothesized that social background creates the starting point for the development of a health-related lifestyle and the educational track an individual is going to follow. One's background, on the one hand, offers economic resources for various lifestyles and education, and, on the other hand, creates a social environment in which lifestyles and educational decisions are made. This environment consists of values, social support and social resources for coping in life. The interplay between lifestyle and education forms a process, during which individuals gradually take up their positions in relation to their future health and social status. This process has a bearing on health differences between social classes.
The phenomenon of health behaviours differing according to the length of education that people are going to acquire is obvious in adolescence (West, 1988, 1991). Young people, who do not feel that education would help them to achieve a good life often turn their interest away from school. This is shown both in the adoption of health-compromising behaviours and in the amount of time spent on leisure, peer groups or work (Willis, 1977; Nutbeam et al., 1989; Mehan, 1992; Persaud and Madak, 1992; Argyle, 1994). Since educational track and school achievement strongly predict an individual's social position in adulthood (Halsey et al., 1980; Timmons, 1988), alienation from school leads to many kinds of difficulties in future life (Education and Research, 1992:1, Persaud and Madak, 1992). Low social class further diminishes a person's coping capabilities and abilities to adopt a healthy way of life (Macintyre, 1986; Jacobsen and Thelle, 1988; Winkleby et al., 1992; Argyle, 1994; Elo and Preston, 1996).
Smoking has a central role in distinguishing individuals who have chosen different educational tracks. Smoking is related to poor school attainment, disaffection with school and adoption of non-conventional values in society (Aarø et al., 1986; Glendinning et al., 1994, 1995). It can be thought that smoking is a strong indicator of a broader lifestyle, which gradually leads to a low level of education and health-damaging activities, with all their consequences (Nutbeam et al., 1989; West, 1991; Glendinning et al., 1992). Aarø et al. (Aarø et al., 1986) have defined lifestyle as `...relatively stable patterns of behaviours, habits, attitudes and values which are typical of the groups one belongs to, or the groups one wants to belong to'. Thus, in order to understand lifestyle, a large number of single, and possibly inter-related, behaviours need to be considered simultaneously. In addition, the role of reference groups is central. It is likely that the role of childhood social class as a reference group for lifestyle diminishes in adolescence when a person is in contact with other groups, e.g. in school and leisure-time. This will be reflected as the weak influence of the family's social class on the association between lifestyle and educational track.
The purpose of this study is to describe whether adolescents who have different health-related lifestyles have been selected into different educational tracks already at the age of 16. We want to identify which particular aspects of lifestyle are typical of adolescents who are at risk of ending up in low social positions or which are typical of those having the best chances of ending up in high positions. The conceptual model of the study is outlined in Figure 1. The associations of lifestyle with educational track at age 16 are assessed with adjustment to socioeconomic background. On the right are the supposed consequences of the process.
|
|
Materials and methods |
|---|
|
TopAbstractIntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
The study is a part of the Adolescent Health and Lifestyle Survey which is a national monitoring system of adolescent health and health behaviours. Data were collected by mailed questionnaires in 1987 from a national sample of 12-, 14-, 16- and 18-year-old Finns. The first inquiry was sent in February and was followed by two further inquiries to the non-respondents. In this paper, only the 16 year olds are considered, because by this age the division into those who continue and those who do not continue their studies has in most cases taken place. All those born between 14 and 31 July were included in the sample which was drawn from the Central Register of the Finnish Population. The total sample of 16 year olds comprised 2977 adolescents. The final number of respondents included in our analyses was 2467. The response rate was 83%, in boys 77% (N = 1183) and in girls 89% (N = 1284). In terms of the geographical region of residence, the response rate was lowest (79%) in Central-West Finland and highest (88%) in North Finland. Self-administered 12-page questionnaires were sent to the sample members. Responding was voluntary and the purpose of the study was explained in a covering letter. The study protocol was accepted by the ethical committee of the Department of Public Health at the University of Helsinki.
Dependent variable
The dependent variable, educational track, is formed by classifying the respondents into five successive categories predicting their social position in adulthood. The first category consists of those who are presumed to have the poorest social prospects, i.e. the lowest probability of reaching a high social position in adulthood. The fifth category consists of those who are presumed to have the best social prospects, i.e. the highest probability of reaching a high social position in adulthood. The categories of educational track are formed according to the type of school the respondents are attending and their achievement.
In the Finnish educational system, after 9 years of compulsory schooling (basic education), a division is made, at the age of 16, into upper secondary schools and vocational or other schools (mostly vocationally or professionally oriented educational institutions) (Central Statistical Office of Finland, 1995). At the time of inquiry, the channel to university degrees and thus supposedly to higher social positions was through upper secondary schools. Achievement is measured by the pupil's own assessment of his or her position in the class, according to the average school marks in the preceding end-of-term school report. Consequently, the first of the successive categories, i.e. those with the poorest social prospects, consists of those who are not attending school at the time of inquiry (8%; N = 2377). The second category consists of those in vocational or other schools who have, at most, average school achievement (28%). The third category consists of respondents in vocational or other schools with above average school achievement (12%). The fourth category consists of those in upper secondary schools who have, at most, average school achievement (25%). The fifth category, i.e. those with the best social prospects, consists of respondents in upper secondary schools with above average school achievement (27%).
Most of the respondents were continuing their education after the compulsory level of schooling. Attending upper secondary schools and doing well in these schools is more typical for girls, while boys more often go to vocational or other schools and get average or below average level reports. Of all respondents, 3.6% are excluded from the classification because information about their school achievement is missing (1.4% in upper secondary schools and 7.0% in vocational schools).
The proportion of respondents attending formal education in this study is similar to the respective proportion in official Finnish statistics for the mid and late 1980s. Also the proportions of those attending upper secondary schools and vocational or other schools are comparable for this age group (Education and Research, 1992:3, 1987:5).
Independent variables
The independent variables are divided into two main groups as follows: sociodemographic background variables and lifestyle variables.
Sociodemographic background
- Father's or other guardian's education: high (12 years or more), middle (from 8 to 11 years), low (at most 8 years).
- Father's or other guardian's occupation is classified by the status classification of the Central Statistical Office of Finland in 1987 (Central Statistical Office of Finland, 1987): upper white-collar workers, lower white-collar workers, farmers, blue-collar workers.
- The urbanization level of the place of residence is defined by the population density: capital area (Helsinki and the adjoining towns), large towns (population over 100 000), small towns, villages (densely populated areas in rural municipalities), sparsely populated rural municipalities (isolated homesteads in rural municipalities).
- The five geographical regions of residence describe the South–North dimension, the most industrialized areas being South and South-West: South (provinces of Uusimaa and Kymi), South-West (provinces of Turku and Pori, Häme), Central-West (provinces of Keski-Suomi, Vaasa), East (provinces of Kuopio, Pohjois-Karjala, Mikkeli), North (provinces of Oulu, Lappi).
- Family type: nuclear family (living with both parents), non-nuclear family (parents not living together, father, mother or both dead, or not living with parents).
- Gender: male, female.
Lifestyle
(1) Health behaviours
- Physical exercise. Organized physical exercise is obtained by summarizing, for each respondent, the total frequencies of participating in exercise organized by (a) schools or workplaces (physical training lessons were excluded), (b) sports clubs and (c) other associations or clubs. Classification is: daily, weekly (at least twice a week, but less frequently than daily), monthly (at least once a month, but not more often than once a week), rarely (less frequently than once a month or no exercise at all). Unorganized physical exercise is a measurement of exercise done alone or with friends or members of the family: classification as above.
- Alcohol use: none (do not drink alcohol or drink at most once a year, but never get drunk), controlled drinking (drink but never get drunk), less-controlled drinking (drink at most twice a month and get drunk at most once a month/drink at least once a week, but get drunk less often than once a month), uncontrolled drinking (drink at least once a week and get drunk at least once a month).
- Smoking: never tried, experimental or occasional (have smoked at most 50 times, but do not smoke daily), 1–9 cigarettes a day, 10+ cigarettes a day.
- Dental hygiene is measured on the basis of the frequency of brushing teeth: several times a day, once a day, 2–5 times a week, at most once a week (or never).
- Drinking coffee: not daily, 1–3 cups a day, 4 cups or more a day.
- Consumption of sugar. Number of sugar lumps used in a cup of coffee is classified as: no sugar (including those who do not drink coffee daily), 1–2 lumps, 3 lumps or more. Consumption of sweets: at most once a week (or never), about 3–4 times a week, daily.
- Consumption of milk fat combines the type of milk a person drinks and the type of fat he/she uses on bread. The three categories are: minor use (do not drink milk or drink skimmed milk and do not use fat on bread, or use margarine or comparable types of spread), medium use (do not suit either the first or the third category), heavy use of milk fat (use whole milk and mostly butter).
- Use of seatbelts when travelling in the front-seat of a car: always, sometimes, never (includes those who do not drive in a car).
- Bedtime: regular, irregular.
(2) Leisure-time activities
- Hobbies. The frequencies of visiting discotheques or dancing places: never, occasionally (more seldom than weekly), at least weekly; sitting in bars or `hanging out' with friends: as above; attending church or other religious meetings: as above.
- The number of hours spent daily on some activities: watching TV or videotapes: occasionally (less than 0.5 h), 0.5–2 h, at least 2 h; reading magazines, newspapers or comic books: as above; listening to music: as above.
(3) Social relations
- The number of close relationships is formed as a combination of three types of social relations, the easiness of talking to mother/father/friends about things that really bother. These are dichotomies with categories easy and difficult (including the ones lacking mother/father/friends, 1.2/7.0/0.1% of the respondents, respectively). The categories of the combinatory variable are: three, two, one, none.
- The starting age of the first time of going steady with someone: not yet, at the age of 15–16, before age 15.
Statistical procedures
The associations between educational track and the independent variables are analysed using polychotomous logistic regression models (Hosmer and Lemeshow, 1986; Agresti, 1990). Because of the ordinal nature of the dependent variable, cumulative logistic models are used. The ordinal dependent variable y takes values 1, 2,..., J according to the category of the response. The model is formulated as (follows):
|
|
).
Two stepwise procedures were carried out. First, of the sociodemographic background variables, those showing independent associations with educational track in univariate analysis (Pearson's 
2-test) are selected into the model. Second, to get the final multivariate model of lifestyle variables which are independently associated with educational track, another stepwise procedure is used. This is done after adjusting for the sociodemographic background variables selected above. The cumulative odds ratios (COR) with 95% confidence intervals (CI) are calculated for the variables showing significant explanatory power in the final model. The groups giving approximately the same COR are combined. As cumulative models examine the relations between the categories of an ordinal type dependent variable, COR thus expresses the incidence of upper categories of the dependent variable as compared to the lower categories on various values of the independent variables.
All the decisions regarding statistical significance are made at the 5% risk level. The analyses are carried out using the PR program in the statistical software BMDP (Dixon, 1992).
Once the final model is found, its parameter values are used to calculate model-based predicted probabilities (see the formula above) of belonging to categories of educational track for persons with different combinations of characteristics. These probabilities are illustrated in Figures 2 and 3 for some combinations.
|
|
The logistic regression functions are computed using only the cases which have values for every variable. For this reason, there is a need to retain in the analyses as many observations for each variable as possible. Thus, for the variable, father's education, missing values (5.2%) are replaced by the most probable value according to father's occupation. For the variable, educational track, the missing values (3.6%), due to a lack of knowledge about the report level, are replaced by using the most probable value of the school report on the basis of school attendance and school type. The distributions of the variables are similar before and after the replacement of missing values, and in the final stepwise procedure there are only slight differences in the decimal parts of the significances and CORs. The proportion of missing values in the final model is 8.2% (16.9% before replacement).
|
Results |
|---|
|
TopAbstractIntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
All the socioeconomic background variables and all the lifestyle variables, except the number of close relations, are significantly associated with educational track in univariate analysis (Table I
). For all the variables, except for urbanization level, geographical region of residence, visiting discotheques or dancing places and hours spent daily on reading magazines or comic books, the associations are significant at the risk level of 0.01%. Thus, it is unlikely that they are caused by the large sample size.
|
The final model is presented in Table II
. In the first stepwise procedure of logistic regression analysis, four socioeconomic background variables have independent associations with educational track and are taken into the final model. Belonging to educational tracks with good social prospects is most typical of girls, of adolescents living with both parents and of adolescents whose fathers have a white collar occupation or a high level of education.
|
In the second stepwise procedure, after adjusting for the sociodemographic variables above, smoking is the lifestyle variable which is most closely associated with educational track. The probability of belonging to educational tracks with good social prospects is high among adolescents who do not smoke daily. Those who brush their teeth regularly, do not drink much coffee, participate in unorganized physical exercise, always use seatbelts, and use moderately or little milk fat and sugar in coffee also have a high probability of belonging to educational tracks with good social prospects.
Of leisure-time activities, the probability of belonging to educational tracks with good social prospects is associated with spending less hours daily watching TV or videotapes and listening to music, and attending church or other religious meetings often.
No variable describing social relations is significant in the final model.
Figure 2
shows model-based predicted probabilities of belonging to each of the five categories of educational track for people representing two different sets of characteristics, i.e. profiles. The profiles are combinations of sociodemographic and lifestyle variable categories. The predicted probabilities are based on cumulative odds ratios of the final model in Table II. A detailed description of the risk profiles in Figure 2 is shown in the Appendix. Figure 2 compares a person with an overall low chance of good social prospects (profile 1) with a person with an overall high chance of good social prospects (profile 2). The difference between profiles 1 and 2 is extreme. The predicted probability of belonging to the educational track with the best social prospects is 0.845 on profile 2 and 0.005 on profile 1. The predicted probabilities of belonging to the educational track with the poorest social prospects is 0.003 on profile 2 and 0.731 on profile 1. The profiles meet each other at the middle point of the distribution of educational track.
Figure 3 shows the effect of sociodemographic background on the association between lifestyle and educational track. Above, the difference between two individuals who both lead a hazardous lifestyle, but who differ according to sociodemographic background, can be seen. The profiles are quite similar, but a favourable background (profile 3) lowers the probability of belonging to the educational track with the poorest social prospects [i.e. of being in the category of not attending school (Table I)]. Otherwise, there is a slight difference in that a favourable background tends to increase the probability of belonging to educational tracks with better social prospects.
Below, there is a comparison between two individuals who both have a high chance of good social prospects according to lifestyle, but of whom one (profile 4) has an unfavourable sociodemographic background. Also here, the difference is most obvious at the extreme of the profile, in that an unfavourable background lowers the probability of belonging to the educational track with the best social prospects. Otherwise, an unfavourable background only slightly increases the probability of belonging to educational tracks with poorer social prospects.
|
Discussion |
|---|
|
TopAbstractIntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
The respondents represent well the entire Finnish population of 16 year olds. Since the proportions of 16 year olds in our study attending upper secondary schools and those attending vocational or other schools are similar to the respective proportions in official Finnish statistics (Education and Research, 1992
:3, 1987:5), the fact that boys were less active than girls in returning the questionnaire has not caused distortion. However, there are no means to assess representativeness according to school performance. It has been found that in health-related surveys the response rate is lower in young men than in young women, and that reluctance to respond is associated with negative health behaviour and poor school performance (Pietilä et al., 1995). If active responding were selective in that direction also here, the associations found would be weak as compared to the situation where everyone would have responded.
Almost every subject could be classified according to educational track and thus the number of missing cases remained small. In forming this variable, it is assumed that its categories reflect people's positions on an ordinal scale. The ordering is based on the knowledge that the selected educational track and school achievement predict an individual's social position in adulthood (Halsey et al., 1980; Timmons, 1988). In the Finnish context, the division into two main levels of education is important, because the channel to university studies at the time of the study was through upper secondary schools.
The selected educational track does not straightforwardly predict the educational level to be attained in adult age. Changes from one track to another and dropping out may occur. In Finland, in 1987, 6% of the students in daytime full-time upper secondary schools dropped out of school, although they could later apply for vocational education. In vocational schools, the drop-out rate was 8% (Education and Research, 1992:14, 1992:16). However, despite these drop-out rates, the overall predictability of adult social position is supposed to be strong (see also Koivusilta et al., 1996).
Among the socioeconomic variables, gender was most closely associated with educational track. Girls were more often on the tracks with good social prospects than boys. It has been observed that nowadays girls achieve better in schools than boys (Cobalti, 1990; Rimpelä et al., 1990; Undheim and Nordvik, 1992). One explanation given for this phenomenon is that girls are often more disciplined and hardworking than boys (Svensson, 1971). Moreover, they are more concerned with the status, rather than the economic value, of education than boys (Shavit and Blossfeld, 1996).
Family type was, in our study, the second most significant background variable. Many other studies have also shown that not living with both biological parents increases the risk of problems in school (Keith and Finlay, 1988; Dawson, 1991; Macintyre, 1992; Mulkey et al., 1992; Persaud and Madak, 1992). In one-parent families, there seems to be more clustering of health-damaging behaviours than in other families (Isohanni et al., 1993). It is probable that in one-parent families guardians do not have much time to spend on furthering the educational success of their children. Also the scarcity of economic resources may decrease the amount of educational material at home (Erikson and Jonsson, 1996).
In this study, too, the importance of social class background on educational track is seen (Bourdieu and Passeron, 1977; Cobalti, 1990; Blackburn and Marsh, 1991; Mehan, 1992; Roberts and Parsell, 1992; Mauger, 1993; Erikson and Jonsson, 1996). Both measures of social class have an independent association with educational track, but the association of father's or other guardian's occupation is stronger than that of his education. On the whole, working-class parents may be content if their son or daughter gets a vocational education, while middle-class parents often want their children to prepare for university studies (Argyle, 1994; Erikson and Jonsson, 1996). Furthermore, the parent's success in life is an encouraging model for a young person, and the lifestyle and values of the upper classes provide the cultural capital needed for success in school (Bourdieu and Passeron, 1977).
Several aspects of lifestyle appear in this study to be more strongly associated with educational track than is sociodemographic background. It is possible that some behaviours better catch the attitudes of young people than the standard measures of background. It is also possible that some adolescents are not able to classify their parents according to occupational class or education. Among behaviours, the central role of smoking is repeated also in many other studies. It is possible that smoking illustrates a broader lifestyle where education is not valued and interest is directed towards other spheres of life (Willis, 1977; Aarø et al., 1986; Persaud and Madak, 1992; Glendinning et al., 1995). This kind of process of withdrawal from school may include other health-damaging habits, low self-esteem and a sense of lack of control over one's own life, with all these features reinforcing each other (Hammarström et al., 1988; Nutbeam et al., 1989; Persaud and Madak, 1992; Nurmi, 1993).
Smoking may be a sign of stress caused by an overload of personal and social development tasks or failure to meet social role expectations (Jarvis, 1994; Hurrelmann and Maggs, 1995). Pressure to succeed academically has been identified as an important source of stress for adolescents (Hurrelmann, 1990). Smoking is associated with many kinds of mental health problems, such as depression (Anda et al., 1990; Covey and Tam, 1990). All this may mean that health-damaging behaviours, health problems and a lack of resources for educational achievement may be closely intertwined already at an early stage of life.
It is also possible that people adopt behaviours for their own purely psychological reasons, like curiosity, the desire to experiment or the images given to various behaviours, e.g. in films and advertisements. Later, people with the same kinds of behaviours join together and then the implication of these behaviours may be further strengthened. Sharing a common habit, like smoking, may add to a sense of togetherness and signify making a distinction from other groups (Bourdieu, 1984). Thus, a habit becomes a source of self-esteem and self-image (Argyle, 1994). The adoption of certain behaviours, especially beginning to smoke, may also be regarded as a rite of transition, a sign of adulthood.
In this study, alcohol use is quite significantly associated with educational track in univariate analysis, but is not selected into the model. This is explained by the way in which the stepwise method at every step selects the most significant predictor from the set of many important, and supposedly correlating, variables (Dixon, 1992). Thus, the inclusion of smoking into the model renders the alcohol variable insignificant. Although these two behaviours are closely associated with each other (Aarø et al., 1995; Pohjanpää et al., 1996), it is also possible that they have different bases in adolescence. Smoking may be more a matter of rebellion and of peer solidarity, while drinking is confined to recreational contexts. Each behaviour also has differing potentials for generating addiction (Biddle et al., 1985). Nowadays, there appear to be two different dimensions in addictive behaviour among Finnish youth. On the traditional dimension, smoking and drinking go together. On the modern, and systematically increasing, dimension, the use of alcohol is involved while smoking is not (Pohjanpää et al., 1996). The association between social group and alcohol use is not as straightforward as that between social group and smoking (Mackenbach, 1992). In Finland, smoking is much more strongly associated with years of education than any indicator of alcohol use (Helakorpi et al., 1995). It seems that smoking in adolescence is a stronger indicator of a lifestyle where education is not highly valued than alcohol.
The major part of other lifestyle variables which are independently associated with educational track are health behaviours, such as putting effort into dental hygiene, eating habits, physical exercise or safety in traffic. The total amount of exercise and participation in most kinds of sports seems to be greatest in the middle classes. This may reflect the activity and motivation to work hard to attain goals. These traits are also behind educational success. Engaging in exercise may also be a sign that a person possesses much energy (Argyle, 1994). On the whole, all the behaviours mentioned above involve a feeling that life is under control to the extent that it is worth while taking care of one's health and abstaining from dangerous situations, and that discipline and self-control lead to rewards in the future. This is a part of middle-class culture that values ambition, individual responsibility, cultivation of skills, postponing immediate satisfaction and planning for the future. (Argyle, 1994). We conclude that, regardless of the original factors behind these behaviours, young people who, at the age of 16, share the same health-related lifestyle, also share some common attitudes towards education.
Of leisure-time activities, the amount of time spent listening to music and watching TV or videotapes divides the respondents. These are relaxation-oriented and passive ways to pass the time, and thus spending much time on these activities may be a sign of a low value given to studying and striving for achievements (Persaud and Madak, 1992; Argyle, 1994). Although these activities are to some extent typical features of the common youth culture, they may here be regarded as indicators of different lifestyles. Attending religious meetings as a leisure-time activity seems to indicate an opposite view of the importance of studying and suggest the adoption of a middle-class lifestyle. The middle classes have been found to be more active in public religious behaviour, like church attendance (Argyle, 1994). Thus, these leisure-time activities, together with the health-related behaviours, sharpen the picture of differing adolescent lifestyles.
The finding that the number of close social relations is not associated with educational track is difficult to interpret. There are many studies showing the importance of relations with parents and friends in educational attainment. Friends mediate parts of the effect of social origin on attainment. (Hauser et al., 1983; Jencks et al., 1983). Also, clear differences have been found in educational attainment and involvement in further education, between youth groups with different types of integration into family, school and peers, regardless of social background (Glendinning et al., 1995).
The final model was used to predict probabilities of belonging to categories of educational track for people with different sets of characteristics. By changing the values of item variables, it is possible to describe and compare various combinations of individual characteristics. The figures show that the entity, which is formed by health-related lifestyle and sociodemographic background together, is a powerful indicator of an individual's career chances. An adolescent with the most favourable characteristics clearly stands out through having a probability of 0.845 of belonging to those who will get the strongest educational basis for reaching a good social status. An unfavourable socioeconomic background would weaken this probability. Likewise, a good home background somewhat improves the chances of adolescents who have the poorest profile of behaviours. The future prospects are gloomiest for the slightly less than one-tenth of the cohort who will remain without further education. They are likely to remain outside the labour market and must rely on social security for their living. These people, who have unhealthy behaviours and who do not have their paths smoothed by their families, are in danger of becoming marginalized from the society in many ways which will influence also their health as adults.
The figures show that, at the age of 16, the influence of home background on the association between lifestyle and educational track is greatest at the extremes of the track variable. Background may have been the original generator of an individual's lifestyle and educational decisions but, in adolescence, the pattern of behaviours closely resembles the lifestyle of the individual's future social group. Thus, it seems that people, already at early stages of their lives, begin to follow behavioural and educational tracks leading to different positions in relation to health and social class in adulthood (Illsley, 1955; Kuh and Cooper, 1992; Argyle, 1994; Glendinning et al., 1994; Helakorpi et al., 1995).
By and large, the study gives evidence of a strong association between health-related lifestyle and educational track in adolescence. However, the stability of these findings from adolescence to adult life needs to be tested by longitudinal study designs.
|
Appendix: The detailed description of the profiles in Figures 2 and 3
|
|---|
|
TopAbstractIntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
Profile 1: The lowest overall chance of good social prospects
Sociodemographic background variables
- Gender: Male
- Family type: Non-nuclear
- Father's occupation: Blue collar worker or farmer
- Father's education: Middle or low
Lifestyle variables
- Smoking: Daily
- Brushing teeth: At most 5 times a week
- Drinking coffee: 4 cups or more a day
- Hours spent daily on watching TV or videotapes: At least 2 h
- Unorganized physical exercise: Rarely
- Use of seatbelts: Sometimes or never
- Use of milk fat: Heavy use
- Hours spent daily on listening to music: At least 2 h
- Attending church or other religious meetings: Occasionally or never
- Use of sugar in coffee: 3 or more lumps
Profile 2: The highest overall chance of good social prospects
Sociodemographic background variables
- Gender: Female
- Family type: Nuclear
- Father's occupation: Upper white collar worker
- Father's education: High
Lifestyle variables
- Smoking: Occasionally or never
- Brushing teeth: Several times a day
- Drinking coffee: Not daily or 1–3 cups a day
- Hours spent daily on watching TV or videotapes: Less than 2 h
- Unorganized physical exercise: Daily, weekly or monthly
- Use of seatbelts: Always
- Use of milk fat: Minor or medium use
- Hours spent daily on listening to music: Less than 2 h
- Attending church or other religious meetings: Weekly
- Use of sugar in coffee: No sugar or 1–2 lumps
Profile 3: A high chance of good social prospects according to sociodemographic background, but a low chance according to lifestyle
Sociodemographic background variables
- Gender: Female
- Family type: Nuclear
- Father's occupation: Upper white collar worker
- Father's education: High
Lifestyle variables
- Smoking: Daily
- Brushing teeth: At most 5 times a week
- Drinking coffee: 4 cups or more a day
- Hours spent daily on watching TV or videotapes: At least 2 h
- Unorganized physical exercise: Rarely
- Use of seatbelts: Sometimes or never
- Use of milk fat: Heavy use
- Hours spent daily on listening to music: At least 2 h
- Attending church or other religious meetings: Occasionally or never
- Use of sugar in coffee: 3 or more lumps
Profile 4: A low chance of good social prospects according to sociodemographic background, but a high chance according to lifestyle
Sociodemographic background variables
- Gender: Male
- Family type: Non-nuclear
- Father's occupation: Blue collar worker or farmer
- Father's education: Middle or low
Lifestyle variables
- Smoking: Occasionally or never
- Brushing teeth: Several times a day
- Drinking coffee: Not daily or 1–3 cups a day
- Hours spent daily on watching TV or videotapes: Less than 2 h
- Unorganized physical exercise: Daily, weekly or monthly
- Use of seatbelts: Always
- Use of milk fat: Minor or medium use
- Hours spent daily on listening to music: Less than 2 h
- Attending church or other religious meetings: Weekly
- Use of sugar in coffee: No sugar or 1–2 lumps
|
Acknowledgments |
|---|
This study was supported by the Ministry of Social Affairs and Health, Finland.
|
References |
|---|
|
TopAbstractIntroductionMaterials and methodsResultsDiscussionAppendix: The detailed...References |
|---|
Aarø, L., Laberg, J. and Wold, B. (1995) Health behaviours among adolescents: towards a hypothesis of two dimensions. Health Education Research, 10, 83–93.
Aarø, L. E., Wold, B., Kannas, L. and Rimpelä, M. (1986) Health behaviour in schoolchildren. A WHO cross-national survey. A presentation of philosophy, methods and selected results of the first survey. Health Promotion, 1, 17–33.
Adler, N. E., Boyce, T., Chesney, M. A., Folkman, S. and Syme, S. L. (1993) Socioeconomic inequalities in health. No easy solution. Journal of American Medical Association, 269, 3140–3145.[Abstract]
Agresti, A. (1990) Categorical Data Analysis. John Wiley, New York.
Anda, R. F., Williamson, D. F., Escobedo, L. G., Mast, E. E., Giovino, G. A. and Remington, P. L. (1990) Depression and the dynamics of smoking. A national perspective. Journal of American Medical Association, 264, 1541–1545.[Abstract]
Argyle, M. (1994) The Psychology of Social Class. Routledge, London.
Biddle, B. J., Bank, B. J., Anderson, D. S., Hauge, R., Keats, D. M., Keats, J. A., Marlin, M. M, and Valantin S. (1985) Social influence, self-referent identity labels, and behavior. The Sociological Quarterly, 26, 159–185.
Blackburn, R. M, and Marsh, C. (1991) Education and social class: revisiting the 1944 Education Act with fixed marginals. British Journal of Sociology, 42, 507–536.
Bourdieu, P. (1984) Distinction. A Social Critique of the Judgement of Taste. Harvard University Press, Cambridge, MA.
Bourdieu, P. and Passeron, J. C. (1977) Reproduction in Education, Society and Culture. London, Sage.
Central Statistical Office of Finland (1987) Classification of Occupations. Handbooks 14. Central Statistical Office of Finland, Helsinki.
Central Statistical Office of Finland (1995) Statistical Yearbook of Finland 1995. Official Statistics of Finland, Helsinki.
Cobalti, A. (1990) Schooling inequalities in Italy: trends over time. European Sociological Review, 6, 199–214.
Covey, L. S. and Tam, D. (1990) Depressive mood, the single-parent home and adolescent cigarette smoking. American Journal of Public Health, 80, 1330–1333.
Dawson, D. A. (1991) Family Structure and children's health and well-being: data from the 1988 national Health Interview Survey on Child Health. Journal of Marriage and Family, 53, 573–584.
Dixon, W. J. (ed.) (1992) BMDP Statistical Software Manual. University of California Press, Berkeley, CA.
Education and Research (1987:5) Oppilaiden ikä vuonna 1985. Central Statistical Office of Finland, Helsinki.
Education and Research (1992:1) Tutkinnon suorittaneiden jatko-opinnot ja työhön sijoittuminen vuonna 1988. Central Statistical Office of Finland, Helsinki.
Education and Research (1992:3) Oppilaiden ikä ja osuus väestöstä 1990. Central Statistical Office of Finland, Helsinki.
Education and Research (1992:14) Lukiot syyslukukaudella 1992. Central Statistical Office of Finland, Helsinki.
Education and Research (1992:16) Ammatilliset oppilaitokset syyslukukaudella 1992. Central Statistical Office of Finland, Helsinki.
Elo, I. T. and Preston, S. H. (1996) Educational differentials in mortality: United States 1979–85. Social Science and Medicine, 42, 47–57.
Erikson, R. and Jonsson, J. O. (1996) Introduction. Explaining class inequality in education: the Swedish test case. In Erikson, R. and Jonsson, J. O. (eds), Can Education Be Equalized The Swedish Case in Comparative Perspective. Westview Press, Oxford.
Glendinning, A., Love, J. G., Hendry, L. B. and Schucksmith, J. (1992) Adolescence and health inequalities: extensions to Macintyre and West. Social Science and Medicine, 35, 679–687.
Glendinning, A., Shucksmith, J. and Hendry, L. (1994) Social class and adolescent smoking behaviour. Social Science and Medicine, 38, 1449–1460.
Glendinning, A., Hendry, L. and Schucksmith, J. (1995) Lifestyle, health and social class in adolescence. Social Science and Medicine, 41, 235–248.
Halsey, A. H., Heath, A. F. and Ridge, J. M. (1980) Origins and Destinations. Family, Class and Education in Modern Britain. Clarendon Press, Oxford.
Hammarström, A., Janlert, U. and Theorell, T. (1988) Youth unemployment and ill health: results from a 2-year follow-up study. Social Science and Medicine, 26, 1025–1033.
Hasan, J. (1989) Way-of-life, stress, and differences in morbidity between occupational classes. In Fox, J. (ed.), Health Inequalities in European Countries. Gower, Aldershot.
Hauser, R. M., Tsai, S. L. and Sewell, W. H. (1983) A model of stratification with response error in social and psychological variables. Sociology of Education, 56, 20–46.
Helakorpi, S., Berg, M. A., Uutela, A. and Puska, P. (1995) Health Behaviour among Finnish Adult Population, Spring 1995. Publications of the National Public Health Institute, Helsinki.
Hosmer, D. W., Jr and Lemeshow, S. (1989) Applied Logistic Regression. John Wiley, New York.
Hurrelmann, K. (1990) Health promotion for adolescents: preventive and corrective strategies against problem behavior. Journal of Adolescence, 13, 231–250.[ISI][Medline]
Hurrelmann, K. and Maggs, J. L. (1995) Health impairments in adolescence: The biopsychosocial `costs' of the modern life-style. In Neubauer, G. and Hurrelmann, K. (eds), Individualization in Childhood and Adolescence. Walter de Gruyter, Berlin.
Illsley, R. (1955) Social class selection and class differences in relation to stillbirths and infant deaths. British Medical Journal, ii, 1520–1524.
Isohanni, M., Oja, H., Moilanen, I., Rantakallio, P. and Koiranen, M. (1993) The relation between teenage smoking and drinking, with special reference to non-standard family background. Scandinavian Journal of Social Medicine, 1, 24–30.
Jacobsen, B. K. and Thelle, D. S. (1988) Risk factors for coronary heart disease and level of education. American Journal of Epidemiology, 127, 923–932.
Jarvis, M. J. (1994) A profile of tobacco smoking. Addiction, 89, 1371–1376.[ISI][Medline]
Jencks, C., Crouse, J. and Mueser, P. (1983) The Wisconsin model of status attainment: a national replication with improved measures of ability and aspiration. Sociology of Education, 56, 3–19.
Keith, V. M. and Finlay, B. (1988) The impact of parental divorce on children's educational attainment, marital timing, and likelihood of divorce. Journal of Marriage and Family, 50, 797–809.
Koivusilta, L., Rimpelä, A. and Rimpelä, M. (1998) Health-related lifestyle in adolescence predicts adult educational level—a longitudinal study from Finland. Journal of Epidemiology and Community Health, 52, 794–801.[Abstract]
Kuh, D. J. L, and Cooper, C. (1992) Physical activity at 36 years: patterns and childhood predictors in a longitudinal study. Journal of Epidemiology and Community Health, 46, 114–119.[Abstract]
Macintyre, S. (1986) The patterning of health by social position in contemporary Britain: directions for sociological research. Social Science and Medicine, 23, 393–415.
Macintyre, S. (1992) The effects of family position and status on health. Social Science and Medicine, 35, 453–464.
Mackenbach, J. P. (1992) Socio-economic health differences in the Netherlands: a review of recent empirical findings. Social Science and Medicine, 34, 213–226.
Mackenbach, J. P. (1994) Socioeconomic inequalities in health in the Netherlands: impact of a five year research programme. British Medical Journal, 309, 1487–1491.
Marmot, M. G., Davey Smith, G., Stansfeld, S., Patel, C., North, F., Head, J., White, I., Brunner, E. and Feeney, A. (1991) Health inequalities among British civil servants: the Whitehall II study. Lancet, 337, 1387–1393.[ISI][Medline]
Mauger, G. (1993) Young people and schooling in France at the close of the 20th century: reproduction and social change. Journal of Educational Policy 8, 73–85.
Mehan, H. (1992) Understanding inequality in schools: the contribution of interpretive studies. Sociology of Education, 65, 1–20.[ISI]
Mulkey, L. M., Crain, R. L. and Harrington, A. J. C. (1992) One-parent households and achievement: economic and behavioural explanations of a small effect. Sociology of Education, 65, 48–65.
Nurmi, J. E. (1993) Self-handicapping and a failure-trap strategy: a cognitive approach to problem behaviour and delinquency. Psychiatria Fennica, 24, 75–85.
Nutbeam, D., Aarø, L. and Catford, J. (1989) Understanding childrens' health behaviour: the implications for health promotion for young people. Social Science and Medicine, 29, 317–325.
Persaud, D. and Madak, P. R. (1992) Graduates and dropouts: comparing perceptions of self, family, and school supports. The Alberta Journal of Educational Research, 38, 235–250.
Pietilä, A. M., Rantakallio, P. and Läärä, E. (1995) Background factors predicting non-response in a health survey of Northern Finnish young men. Scandinavian Journal of Social Medicine, 23, 129–136.[ISI][Medline]
Pill, R., Peters, T. J. and Robling, M. R. (1995) Social class and preventive health behaviour: a British example. Journal of Epidemiology and Community Health, 49, 28–32.[Abstract]
Pohjanpää, A. K. J., Rimpelä, A. H., Rimpelä, M. and Karvonen, J. S. (1997) Is the strong positive correlation between smoking and use of alcohol consistent over time? A study of Finnish adolescents from 1977 to 1993. Health Education Research, 12, 25–36.
Rimpelä, A., Karvonen, S., Rimpelä, M. and Siivola, M. (1990) Nuorten terveystottumusten väestöryhmittäiset erot ja elinolot 1977–1987. Publications of the National Board of Health, Finland. Health Promotion. Series Original Reports 1/1990. Valtion Painatuskeskus, Helsinki.
Roberts, K. and Parsell, G. (1992) Entering the labour market in Britain: the survival of traditional opportunity structures. The Sociological Review, 8, 726–753.
Rutter, D. R. and Quine, L. (1994) Demographic inputs and health outcomes: psychological processes as mediators. In Rutter, D. R. and Quine, L. (eds), Social Psychology and Health: European Perspectives. Avebury, Aldershot.
Shavit, Y. and Blossfeld, H. P. (1996) Equalizing educational opportunity: do gender and class compete? In Erikson, R. and Jonsson, J. O. (eds), Can Education Be Equalized? The Swedish Case in Comparative Perspective. Westview Press, Oxford.
Svensson. A. (1971) Relative Achievement. School Performance in Relation to Intelligence, Sex and Home Environment. Almqvist and Wiksell, Stockholm.
Taylor, S. and Repetti, R. L. (1997) Health psychology: what is an unhealthy environment and how does it get under the skin ? Annual Review of Psychology 48, 411–447.[ISI][Medline]
Timmons, G. (1988) Education, Industrialization, and Selection. Routledge, London.
Undheim, J. O. and Nordvik, H. (1992) Socio-economic factors and sex differences in an egalitarian educational system: academic achievement in 16-year-old Norwegian students. Scandinavian Journal of Educational Research, 36, 87–98.
West, P. (1988) Inequalities ? Social class differentials in health in British youth. Social Science and Medicine, 27, 291–296.
West, P. (1991) Rethinking the health selection explanation for health inequalities. Social Science and Medicine, 32, 373–384.
Willis, P. (1977) Learning to Labor. Columbia University Press, New York.
Winkleby, M. A., Jatulis, D. E., Frank, E. and Fortmann, S. P. (1992) Socioeconomic status and health: how education, income and occupation contribute to risk factors for cardiovascular disease. American Journal of Public Health, 82, 816–820.
Received on April 4, 1997; accepted on April 16, 1998
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
< |