How many people actually have gender dysphoria
Gender and Health Inequalities - Social Networks in the Context of Health and Health Behavior
There are significant differences in morbidity (disease frequency) and mortality (mortality) between men *1 and women *.
Up until puberty, male * adolescents are more likely to have health problems.
During puberty, girls * suffer from chronic and mental illnesses and male * adolescents are more likely to suffer from acute and life-threatening diseases (e.g. HIV).
Boys * and men * have riskier health behaviors.
The research field mainly relates to the binary gender of men and women. Studies on trans *2 and cross3 People are rare in this area.
Networks have a gender-specific effect on risk behavior.
Women * provide more and more time-consuming social support, even in the event of illness.
After being widowed, networks have both negative and positive effects. These are gender specific.
1 Gender as an inequality category
Gender is now one of the most important categories in social science discourse. The gender category permeates and shapes all areas of social life and, in its function of a horizontal dimension of inequality, plays a significant role as a social order and social positioning category (Babitsch 2009; Rose 2015). Both for the female * and for the male * gender "there are gender-specific ideas of normality, behavioral standards and staging scripts that they have to constantly come to terms with in the course of their lives" (Rose 2015, p. 63). In our everyday knowledge, the bisexuality of humans as well as the gender affiliation established from birth (and mostly pregnancy) and the heterosexuality implied by it are mostly accepted and practiced as self-evident and natural (Wetterer 2004).
However, the gender category is a socially structural and socially constructed as well as historically and socially grown phenomenon that is (re) produced in social and everyday interactions and actions (doing gender). Gender divides individuals into two different groups, which are based on both a biologically bound assignment and social ascription processes (Winker and Degele 2010).
An important step in the consideration and analysis of gender is the separation into the two components sex (the gender assigned at birth based on biological characteristics) and gender (as a social, cultural dimension and gender identity) and the associated possibility of "questioning the notion of natural differences between women and men associated with biological gender and reflecting on them in the context of societal modes of production" (Babitsch 2009, p. 284) . The gender-specific behaviors are thus subject to a social character and are also dependent on the respective cultural, historical and social conditions. Furthermore, the existing social gender inequalities cannot be justified by biological gender differences alone (Babitsch 2009; Degele 2010).
1.1 Gender and Health
Since the 1970s, the issue of gender and its influence on health has gained in influence both in research and in medical practice (Babitsch 2009; Kuhlmann 2016). A large number of studies have shown that there is sometimes a very pronounced gender difference in terms of health. In other words, men * and women * seem to differ significantly in terms of morbidity (frequency of illness) and mortality (death rate), the development processes of diseases and the course of disease and health behavior (e.g. Robert Koch Institute 2015). Studies on trans * people are rather rare. Most of the available research covers the whole group LGBT * I * Q: "Lesbian, Gay, Bisexual, Transgender, Intersexual and Queer".
Life expectancy and mortality
A homogeneous pattern of life expectancy can be discerned in most countries around the world: the life expectancy of women * is generally higher than that of men * (Kolip and Hurrelmann 2002).4 In Germany, too, the mean life expectancy of women * at birth is currently 83.1 years, that of men * 78.3 years (Robert Koch Institute 2015; Federal Statistical Office 2019e). In recent years, the gender differences have been converging in favor of the male * gender (Kolip and Hurrelmann 2002; Lampert et al. 2017), which is attributed to the increase in health-risk behavior among women * (e.g. increasing number of women who smoke *) . However, the shorter life expectancy of the male * gender persists. Worldwide, male * infants also show a higher risk of death than female * infants (WHO 2019). The unfavorable mortality statistics of the male * sex also continue in the further course of life and become particularly clear between the ages of 25 and 65. In Germany, almost twice as many middle-aged men * (86,654) died in 2016 as women * (46,815) (Federal Statistical Office 2019a). A more health-risky behavior of men * is often cited as the cause of the gender difference with regard to excess mortality (Hurrelmann and Quenzel 2011; Kolip and Hurrelmann 2002; Sieverding 2005; Robert Koch Institute 2015). Babitsch (2009) adds that a large number of studies indicate greater socio-economic differences in life expectancy of women * and men *.
There are also clear differences between the sexes for the various causes of death. Women * die more often from cardiovascular diseases. However, they die less often from malignant neoplasms.5 There are only minor gender differences in respiratory or digestive system diseases (Robert Koch Institute 2015). A huge gender difference can be confirmed for the suicide rate. In 2016, 9,838 people ended their lives, with the proportion of men * at 75% being three times as high as the proportion of women * at 25% (Federal Statistical Office 2019b, c).
Morbidity in the curriculum vitae
Gender-specific differences are also visible in morbidity. Hurrelmann and Quenzel (2011) stated that even in childhood, i.e. H. From the first year of life to the onset of sexual maturity around the age of twelve, there are health differences between the sexes: boys * do worse than girls * in most health indicators. As a result, more boys * (4598) than girls * (3659) were examined by medical staff for health disorders up to the age of 15 in 2017 (Hurrelmann and Quenzel 2011; Robert Koch Institute 2015; Federal Statistical Office 2019a). Gender-specific differences in childhood can also be shown in the frequency of illness (Hurrelmann and Quenzel 2011; Kolip and Hurrelmann 2002). As an example, according to the Federal Statistical Office (2019a), in 2017 boys * aged 1–15 were more likely than girls * to contract leukemia, epilepsy, and chronic diseases of the lower respiratory tract, such as bronchial asthma, and were more often overweight as well Sleep disorders. The male * genders are also more susceptible to mental illnesses (Federal Statistical Office 2019a; Robert Koch Institute 2015; Hurrelmann and Quenzel 2011).
Only with the onset of puberty does this situation change within a few years. Girls * aged 15 and over are significantly more likely than boys * to develop psychosomatic and physiological complaints (Kolip and Hurrelmann 2002): For example, girls * aged 15-18 suffer more often than boys * from headaches, abdominal and pelvic pain Dizziness and dizziness and eating disorders (Federal Statistical Office 2019a). With the onset of puberty, the use of medical consultations and treatment changes, which are now used by more girls * than boys * (Hurrelmann and Quenzel 2011; Federal Statistical Office 2019a).
Puberty marks a turnaround both in the use of medical assistance and in health and illness, but the gender-specific tendency continues into adulthood (18–65 years) (Kolip and Hurrelmann 2002). In this regard, the Robert Koch Institute (2015), Lampert et. al (2017) and Babitsch (2009) found a gender-specific difference in subjective self-assessment of health. 72.9% of all middle-aged women * rate their health as good or very good, whereas it is 76.6% for men * (Robert Koch Institute 2015).
The presence of one or more chronic diseases also leads to a gender-specific difference. Kolip and Hurrelmann (2002), Sieverding (2005) and Regitz-Zagrosek (2018) emphasize that the female * disease profile is more likely to be caused by chronic diseases as well as psychosomatic and psychological impairments (e.g. thyroid diseases, depression, eating disorders, migraines, hypertension , Gall bladder diseases, osteoarthritis, osteoporosis, coronary heart diseases); the male *, on the other hand, through acute and life-threatening diseases (e.g. HIV infection, malignant neoplasms of the digestive organs as well as the lungs and bronchi, emphysema, liver cirrhosis, coronary heart disease). A meta analysis of trans * people indicates that trans * men in particular6 are severely affected by HIV / AIDS. There are also connections to other risk factors such as prostitution or discrimination (Herbst et al. 2008). Overall, LGBT * I * Q people show a high risk of intestinal diseases (e.g. Giardia, amoeba), hepatitis A and B, human papilloma viruses7 and anal cancer (Dean et al. 2000).
Coronary heart disease (CHD), which, according to Kuhlmann (2016), is the best examined area under the gender aspect, is of particular importance with regard to gender-specific differences in terms of morbidity. According to Kuhlmann (2016) and Regitz-Zagrosek (2017, 2018), there are gender-specific differences in almost every CHD degree. Regitz-Zagrosek (2017), for example, cites differences in risk factors.8 Kuhlmann (2016) brings in further examples and refers to German and international studies. According to this, “women with CHD are treated less evidence-based [...], [receive] invasive diagnostics less often, [are] informed about preventive measures (eg smoking cessation programs) less often and differently” (Kuhlmann 2016, p. 189).
Differences between the sexes can also be recorded with regard to mental illness. According to the data from the Robert Koch Institute (2015), the prevalence of anxiety disorders in women * is 21.3% higher than in men * (9.3%). A similar picture emerges for depressive disorders and chronic stress (Hapke et al. 2013). Even if there is hardly any research on completed suicide or suicide risk among trans * people, the very detailed review by Haas et al. (2010) found an increased risk of suicide as well as an increased number of completed suicides for trans * persons. This is especially true for young adolescents (Mustanski et al. 2010). Trans * people also have higher prevalence rates than the general population, among other things. for depression and anxiety disorders (Nieder et al. 2016).
With regard to alcohol addiction, men * seem to be more affected than women *. Pabst et al. (2013) found 2.0% of women * and 4.8% of men * in Germany to be addicted to alcohol in 2012. In 2017, the hospital diagnosis statistics registered 85,283 women * and 228,928 men * 228,928 inpatient treatment cases due to mental and behavioral disorders caused by alcohol (Federal Statistical Office 2019a).
In old age, there is hardly any general gender difference to be seen. With regard to multimorbidity, the female * gender scores significantly worse, i.e. That is, women * suffer more often than men * from multiple chronic diseases with increasing age, e.g. B. osteoporosis, osteoarthritis and heart failure. In the male * gender, life-threatening diseases such as heart attacks and chronic obstructive pulmonary diseases occur even in old age (Iller and Wienberg 2012; Federal Statistical Office 2019a).
With regard to the subjective assessment of one's own state of health, gender differences can still be proven: Men * are generally more satisfied; however, health satisfaction declines faster with increasing age (Iller and Wienberg 2012).
For trans * people, Graham et al. (2011) combine individual aspects in the various phases of life, but according to their own information they can hardly provide any results.
1.2 Gender and health behavior
According to Kuhlmann (2016), cultural and social factors influence the use of health services and sometimes lead to significant gender differences in almost all areas of the health care system. For example, more women * take part in health courses offered by adult education centers and health insurance companies (Kuhlmann 2016; Robert Koch Institute 2015), which, however, presumably also primarily target the needs of women * and thus disadvantage men * (Kuhlmann 2016).
According to Prütz and Rommel (2017), there is also a considerable difference in the use of outpatient medical care, but there is also a clear convergence of the sexes with increasing age. A gender difference is particularly evident in young adulthood: 90.4% of 18 to 29 year old women * and only 78.4% of men * used a benefit in the last twelve months. However, there are hardly any differences between the 65-year-olds and older people. According to Kuhlmann (2016), more recent studies and differentiated analyzes also refute the assumption that men * care less about their health.
The use of psychiatric and psychotherapeutic services has a special position in the context of gender differences. According to Barry and Yuill (2012) and Rommel et al. (2017), men * are particularly reluctant to consult or report a mental illness, which the authors attribute to the prevailing social gender stereotype (Barry and Yuill 2012).
Smoking behavior is one of the major health risk factors and the leading cause of premature mortality. According to Zeiher et al. (2017) and here the data of the GEDA study 2014/2015-EHIS a slightly stronger (occasional) smoking behavior in men * (27.0%) than in women * (20.8%). According to the Robert Koch Foundation (2015), however, the gender smoking quota has converged due to the increase in female * smokers over the past 20 years. Changes in gender roles and stereotypes are assumed to explain the rise in the female * smoking rate (Sieverding 2005; Kolip and Hurrelmann 2016; Bartley 2017, see below).
Alcohol consumption shows considerable gender-specific inequalities in all ages as a risk factor: Orth and Töppich (2015) report that in 2014 8.5% of girls * between the ages of 12 and 17 and 14.9% of boys * of the same age Have consumed hazardous amounts of alcohol at least once a week for the past 12 months. This gender difference is also evident in adulthood (Lange et al. 2017). A significant influence of social differences and alcohol consumption on the sexes can be demonstrated (Lange et al. 2017). For example, women * in the upper educational group show a higher prevalence of risky alcohol consumption in all age groups than women * from lower educational groups; the same applies to the male sex *.
Hardly any research has examined substance use among trans * adolescents and adolescents. As already mentioned, existing studies mostly relate to the group of LGBT * I * Q people. However, a study of young LBT * I * Q women * in San Francisco shows that substance use among trans * women9 Is very common in adolescents and this is significantly related to psychosocial risk factors (Rowe et al. 2015). A long-term study in the USA was able to show that alcohol consumption increased linearly over time. In male * LGBT * I * Q adolescents *, the increase tended to be faster than in female * adolescents (Newcomb et al. 2012). The abuse of prescription opioids and sedatives is evident in LGBT * I * Q adolescents at a young age (Kecojevic et al. 2012).Following on from this, there is evidence in another US study that the abuse of prescription drugs is relatively common in LGBT * I * Q adults and is strongly associated with emotional distress (Benotsch et al. 2013).
1.3 Selected explanatory approaches in the context of gender-specific health differences
Finding the cause is just as difficult as the gender-specific health differences could be shown. Many questions have not yet been clarified in this context. Of the various explanatory approaches currently being discussed, three are presented below.
Gender-specific role concepts and stereotypes
The influence of societal constructions of gender on health has long been discussed extensively in scientific discourses (Kolip and Hurrelmann 2016; Kuhlmann 2016; Sieverding 2005; Babitsch 2009; Barry and Yuill 2012; Bartley 2017; Regitz-Zagrosek 2017, 2018 and others). According to Sieverding (2005), there is largely a consensus on "That the gender differences in physical health and illness are mainly due to gender differences in health-related behavior, especially in the higher risk behavior of men" (Sieverding 2005, p. 57). Health-related behavior is in turn influenced by a large number of socio-cultural factors. In this context, social gender roles and stereotypes are assigned a key function (Sieverding 2005). For example, women * are still assigned a more caring and health-conscious role in connection with health. On the other hand, the construction of male * gender is still based on being able to or having to solve health problems independently and without outside help, or to have to, in order to be able to maintain control of one's own performance (Kolip and Hurrelmann 2002).
Social gender stereotypes and roles also affect how other people are judged. It is assumed that patients are perceived and treated differently by medical specialists depending on their gender. Studies indicate that healthcare professionals take male * complaints more seriously. In contrast, women * seem to be more likely to suspect mental illnesses and treat them accordingly (Kolip and Hurrelmann 2002; Sieverding 2005; Kuhlmann 2016). A qualitative study shows that hospital staff often react to the health needs of trans * patients with uncertainty, which can then be expressed in stigmatization. In the case of trans * people, this in turn leads to the view that their needs are not understood (Poteat et al. 2013). A review of 17 articles on the attitudes of nurses towards LGBT * I * Q patients confirmed the result of the disadvantage (Dorsen 2012, on discrimination see also Grant et al. 2011).
Poverty and social inequality
Poverty and social inequality have central effects on the health and life expectancy of the sexes and at the same time lead to gender-specific differences. Women * still receive an average of 21% less salary than men *, work part-time with 49% almost twice as often as men * (11%) and are still more affected by poverty than men * (WSI 2017). These gender-specific inequalities have a significant impact on health.
In this context, Kuhlmann (2016) refers, for example, to the German health system, which is characterized by high quality standards and a high density of care as well as low access barriers. Nevertheless, there are systemic barriers that lead to differences in the access opportunities of men * and women *. As an example, Kuhlmann (2016) cites the increasing financial burdens on users in connection with the own contribution to the health insurance contributions. The co-payment is currently 14.3% in Germany, in 2010 it was still 13% (WHO 2010; Federal Insurance Office 2017). So there is a slight incline. "Here it is obvious that women as a group are more frequently and more severely affected than men by an increasing share of the health-related costs due to the income differences." (Kuhlmann 2016, p. 188). This applies even more to the group of older women *, since women * receive a lower pension with an average of EUR 643 per month than men * (EUR 1154) and are therefore more often affected by poverty in old age (ibid., WSI-Gender Daten Portal 2018) . A US study found that the unemployment rate among trans * people is twice as high as the general population. This means that they are less likely to have health insurance on the one hand and to be generally insured by a company on the other (Grant et al. 2011).
2 Gender and Social Networks - An Overview
Looking back historically, it was assumed in the 1970s that women * and men * have different attitudes towards social contacts (e.g. Miller 1976), without network studies with large samples being available until then10who supported this thesis. It was not until the 1980s that quantitative and qualitative research with an explicit gender focus increased, and gender also became more and more important in network research. Gender has meanwhile become an inequality variable that has been examined very well in comparison to most of the other characteristics presented here in this volume. Due to the large number of studies and also because gender is often used as a control variable in quantitative network research, this list cannot be representative, but only reproduce a short excerpt from the research and point out empirical approaches and gaps. It should be noted that the search for differences is still primarily based on a binary difference schema of man * / boy * vs. woman * / girl *. B. transgender or queer have hardly been taken into account so far.
2.1 Social networks and age
According to friendship and school research, gender is an important variable for friendship formation. Already for Preschoolers can Martin et al. (2013) show that the choice of play partner falls disproportionately on same-sex children. Also with regard to the networks of young people growing (McPherson et al. 2001) and in the first years of secondary school (Lubbers and Snijders 2007) there is still a very strong separation between the sexes (high gender homophilia), which although decreasing over the years, is still maintained. As people get older, these homogeneous networks slowly dissolve and gender-heterogeneous groups develop (Feiring 1999). Studies by Lubbers and Snijders (2007) also show a low proportion of love or sexual relationships in secondary school, while these are more pronounced in high school (Bearman et al. 2004). In both studies, these sexual or relationship networks are predominantly heterosexual and thus increase the proportion of opposite-sex alteri in the network.
For elderly, and here, for example, in the family networks of older Mexicans, the study by Fuller-Iglesias and Antonucci (2016) shows no gender differences (proximity, shares in the network). In contrast to this, Schwartz and Litwin (2018) use the pan-European longitudinal survey “Health, Aging, and Retirement in Europe” (n = 13,938) for over 65-year-olds, especially for women *, an increase in network relationships over time, which parallel to this less are involved in family networks.
2.2 Life cycle and the composition of social networks
With regard to the life cycle, different research results paint the following picture. In the study by Fischer and Oliker from 1983, there are a few differences between the sexes after adolescence. Women * are more in contact with relatives, while men * name more employees and colleagues as network partners. A larger correlation becomes clear with regard to the life cycle phases. At a early marriage and parenting, the friendship networks among women * compared to men * shrink more. After birth these decrease compared to women * especially men *. "Further evidence suggests that this interaction effect can be explained by both structural and dispositional factors, the former working to reduce women’s friendships relative to men’s in the earlier period and the latter expanding their friendships later on" (ibid., p. 132). Here Munch et al. (1997) found that social norms regarding child-rearing in Western countries have an impact on network structures. While the birth of a child did not have a statistically significant influence on the size of the network of men *, it did have a significant negative influence on the size of the network of women *.
2.3 Gender differences in the network structures with regard to the general population
In addition to studies on the topic of phases of life, there have been various studies since the 1980s that investigate the question of gender differences with regard to social networks in the general population. Mention should be made here of the much-cited study by Fischer (1982) "To Dwell Among Friends - Personal Networks in Town and City", which highlights the effect of gender on the networks. "Women tend to be involved in networks with more relatives and to have more intimate ties than did otherwise similar man. Young women, particular mothers, were more constricted in various ways, such as in the number of the “just friend” they had […] “ (Fischer 1982, p. 253).
On the other hand, and in contradiction to Fischer (1982), Gillespie et al. (2015) found no significant gender-specific differences in the number of friends, the number of ages with whom one celebrates birthdays, intimate matters (e.g. sex life) or problems discussed late at night. However, the number of friendships varied considerably according to marital status, age and parental status (see above). It is noticeable that each of the respondents can name at least one close friend.
Other studies with the same focus drew on the data from the General Social Survey (GSS) from the USA to find out how the networks in the US population can be described. The study by Marsden (1987) examines the question of differences with regard to the variables age, education, “race”, gender and size of the place of residence, etc. As a result, the networks of young, well-educated and metropolitan residents seem to be the largest. Gender differences are found primarily in the composition of the network of relatives and non-relatives; the proportion of family members is greater for women *. Similar results are shown in a somewhat older study by Moore (1990). Even after controlling the variables related to employment, social structure, family and age, women * had a greater proportion of family relationships and a smaller proportion of acquaintance relationships in the network as well as a greater variety of family relationships than men *. These differences are attributed to different structural contexts and locations of relationships, which exert certain possibilities and limitations on the formation of close social relationships. The gender differences in network composition and structure disappear when employment and family status as well as age are statistically controlled. Nevertheless, the empirical finding remains that the networks of women * contain a higher number, a higher proportion and a greater diversity of family relationships than the networks of men *.
A study in Singapore also shows that men * and women * are more likely to make professional contacts that are dominated by their own gender (bipolar: man * or woman *). For example, women * are more likely to meet nurses because they are overrepresented in nursing, whereby different phases of life have an influence. From the birth of a child, women * come into contact with occupational groups in which they are underrepresented, such as B. teachers, which in turn affects the composition of the network (Chua et al. 2016).
The gender aspect seems to have lost strength in its effect on the differences in network formation in recent years. While women * have somewhat larger networks than men * and have more conversations with relatives about important matters, they now also have more and more relationships outside of the family. Women * no longer have a clearly kinship-oriented discussion network than men * and are no longer socially isolated as often (McPherson et al. 2006). This is also confirmed by Fuller-Iglesias and Antonucci (2016) for 18- to 99-year-old Mexicans.
In view of the contradicting research results, it is questionable whether there really are differences in terms of the networks. For example, some critics point to the strong interviewer effect in the GSS survey (Fischer 2009), while others question the name generators used and note, for example, that women * may have more important things to discuss than men * and therefore may have a larger network (Bearman and Parigi 2004).
2.4 Network Resources and Gender Differences
In addition to the structural description, many studies look into the question of which resources the networks can make available. This happens on different levels:
On a general social level. The aim here is to find out to what extent the distribution of resources in the general population differs between the sexes.
At the organizational level, the extent to which integration into social networks influences - mostly professional - success is examined.
Social support and resource allocation
Gillespie et al. (2015) show that men * as well as women * can equally fall back on emotional support. The same can be seen in Moore (1990). Bearman and Parigi (2004) point out, however, that against the background of “important things to discuss” women * more Specify people as men *. The study on social support by Turner and Marion (1994) supports both a life cycle and a gender effect: women * state that they receive more social support from employees, relatives and friends than men *. Contrary to this, Vyncke et al. (2014) based on the available social capital of women * and men *. Men * can activate significantly more resources in the network, report more potential support relationships and more network partners * who promote healthy lifestyles. Hobfoll and Vaux (1993), on the other hand, use various studies to conclude that women * are more involved in social support interactions, they act more skillfully in support processes and therefore often have more and more intimate relationships and larger support networks. Women * spend more time in social interactions, are more likely to share feelings and personal concerns, and are more likely to report receiving social support.
Walen and Lachman (2000) found in their study of 2348 adults (25–75 years of age) involved in two-way relationships that women * report more support from family and friends, whereas men * receive more support from their partner. In addition, Diewald (1991), based on the evaluation of five representative population surveys, states that women * have more contact persons than men * in most forms of life. This was especially true for single, single parents, divorced and widowed women *. Women * are looking for, so also Barker et al. (1990), more likely than men * to support close and distant relatives as well as friends and neighbors.
Who women * and men * prefer when seeking help seems contradictory. Evidence Antonucci et al. (1998) and Lenz (2003) that their own gender is favored when looking for support, other scientists also show contrary findings. Although women * tend to consider family helpers * more often, such as sisters *, or female * helpers outside the family, such as the neighbor * (Nestmann and Schmerl 1992), women * are generally the central "donors *". In the study by Veiel and Herrle (1991), students as well as depressed patients and parents of children with cancer named women * than men * as supporters on average.
The gender-specific division of labor is also shown most clearly with regard to help in the event of illness. Both male * and female * respondents named women * many times more often than men * as sources of social support.They are equally important supporters in the case of depression, advice on important life changes and problems with the partner * (Diewald 1991). Nestmann and Schmerl (1992) also name women * more often as helpers *. According to the authors, both men * and women * receive more help from female * helpers than from male * helpers (mother * mentioned more often than father *, daughter * before son *, sister * before brother *). Women *, and especially mothers *, are therefore considered to be the central support bodies for their family (ibid.). Among others in Barker et al. (1990), men * were significantly more likely to use their partner as a supporter in stressful situations. The man's reliance on his partner is also particularly pronounced in older people over the age of 60 (Diewald 1991).
Women * not only act more often as supporters for their men *, but also offer more frequent and more time-consuming support than fathers * for their adult children, according to the study overview Schmids (2014). If they are particularly helpful around the house and with childcare, fathers * tend to support their adult children with shopping, repairs or gardening. With a view to intergenerational relationships, there are also gender differences on the part of the children's support services. Daughters * maintain more frequent contact with their parents than sons *, daughters * take on physically demanding and time-consuming care services more frequently in many countries and provide more support overall. Sons * mainly help their parents with administrative tasks, repairs or financial questions. However, according to Schmid (2014), these differences have so far received little attention in generational research, which is why the "The causes of gender-specific support patterns are still insufficiently researched [are] "(ibid., p. 17)
Network and support studies on the worlds of trans * people are hard to find. However, by way of example, Pflum et al. (2015) found a significant correlation between social support and mental health for trans * people: general social support is significant for both participants in the trans * male spectrum (TMS) and the trans * feminine spectrum (TFS) negatively associated with symptoms of anxiety and depression - d. That is, with increasing social support, feelings of anxiety and depressive moods decreased. However, the negative association between trans community attachment and mental health symptoms was only significant for TFS participants.
Social relationships of men * and women * in organizations
In addition to the general use of networks against the background of social capital and social support theories, there is research on the difference in the “utilization” of social relationships between men * and women * in organizations such as B. Business enterprises or universities. It is assumed here that professional “success” does not only depend on skills, but also on networks. Especially women * seem in this case - among others. through processes of stereotyping (Oehlendieck 2003) - to be disadvantaged (Lyness and Thompson 2000). Most of this research shows that men * have larger work-related networks, are connected to larger clusters, and derive more benefit from these relationships as men * occupy higher positions in hierarchical structures (McGuire 2000).
In contrast, women * seem to be embedded in smaller and less diverse networks that hardly make any resources available. These show a female * homophilia and are mainly occupied by people from lower hierarchical positions. Since the sub-clusters also tend to be more homogeneous, there is an overlap in resources, which can lead to a social capital disadvantage and the reproduction of positions within the network (Lin 2000). Scheidegger and Osterloh (2003), in contrast, conclude that predominantly men * (as people with strong legitimacy) would gain career advantages from structural holes and women * would need cohesive, redundant networks for advancement within the organization. At the same time, women *, as long as only a few female * persons are represented in central positions of higher status, are dependent on network contacts with higher-ranking men * for resource-economical reasons and must therefore differentiate their network contacts - with corresponding costs. In a study overview, they also show the strong homophilia of the respective networks (managers, employees from media companies), whereby v. a. for men * it was the case that their networks consisted primarily of “same sex ties”. It is therefore assumed that women * tend to focus on their individual skills rather than on social capital (Poole and Bornholt 1998), while men * rely more on networks and make better use of resources (Van Emmerik 2006).
3 Gender, Social Networks, and Health Inequalities
3.1 Impact of social capital and social support on health inequalities
The importance of gender in research on health inequalities has been emphasized repeatedly in recent times and is usually linked to the concept of social capital or social support as a central function and an important mechanism of action of social networks (see the article : Social relationships, social capital and social networks and the contribution: Mechanisms of action in social networks). The term “network” is used, if it occurs at all, as a metaphor for supportive relationships or for “supportive” relationships.
There are numerous indications that this social capital and the availability of social support are unequally distributed between men * and women * and that the effect must also be differentiated on a gender-specific basis. This has already been partially discussed in the previous chapter (see above). On the basis of several studies, Underwood (2005) assumes that women * generally receive more support than men * with diseases (bypass surgery, myocardial infarction). They often receive more emotional, but not necessarily material, support over a longer period of time (ibid., See also Hobfoll and Vaux 1993). The effect, on the other hand, is assessed differently. In a Finnish study, for example, there was a positive association between trust (trust) found in women * as well as recreational activities with others in men * and a lower mortality rate. Based on Danish survey data, Ejlskov et al. (2014) found a statistically significant gender difference with regard to the relationship between social capital and mortality. The results show, even after checking socio-economic status, age, health status and health behavior, that women * have a higher level of social capital with a lower risk of mortality (all-cause mortality) was connected. Another theoretically very significant finding of the study is the positive correlation for women * between the frequency of contact with friends and a lower risk of mortality. Kawachi and Berkman (2001) also point to the negative effects of social capital. Accordingly, women * are mentally more stressed by their social commitment and show corresponding symptoms of illness when people with whom they are connected have (health) problems. Subsequently, Sarason et al. (1997) and Antonucci et al. (1998) that women * are more involved in social relationships and, especially if they have larger networks and maintain many close relationships, are more likely to experience stress and negative effects on general life satisfaction. According to Walen and Lachman (2000), this may be due to the fact that women * who are more embedded in social relationships are also more likely to be exposed to negative events in their social environment (e.g. a friend after the loss of a loved one support). They are more likely to perceive other people's needs for help, react to them and act as supporters in crises (Hobfoll and Vaux 1993; Nestmann and Schmerl 1992). In general, the well-being of the women surveyed * is more associated with positive and negative aspects of marital and friendship relationships than with men * (Antonucci et al. 2001).
3.2 Networks and gender differences with regard to health
Beyond this research on social capital, there are also some studies in which a dedicated network perspective is the focus and gender differences play an important or central role. These often focus on certain phases of life and there in particular on the youth phase, which is already comparatively well researched in terms of network analysis (see also the article: "Social networks, health and health inequalities in adolescence") as well as the phase of older age (see also the article : “Social networks and health inequalities in old age”). Some recent findings from these research areas are presented below.
An important issue in adolescence is risk behavior, such as B. Tobacco or alcohol consumption. Both cross-sectional and longitudinal studies can be found here that illuminate gender differences and a. focus on networks in school classes. Network research can show here that specific network properties, such as B. homophilia, ensure that specific health behavior as well as interventions to improve health behavior can spread more or less well (Valente 2012).
For example, Grard et al. (2018) in a cross-sectional study, gender differences in cigarette, alcohol and cannabis consumption among 14- to 16-year-old boys * and girls * at 50 European schools. They show that girls * have a lower prevalence of substance use than boys *. But the gender of the friends also plays a role: if girls have more friends of the opposite sex in their networks (other sex friendships, OSF), they are more likely to use one of the three substances surveyed than girls * who are more friends with girls * (same sex friendhips, SSF). Boys * in OSF are more likely to smoke than boys * in SSF. When boys * consume alcohol and cannabis, SSF is more likely to be associated with the consumption of these substances. The gender composition at school is also important: in schools that are male * dominated, the risk of substance use is higher for boys * and girls *.
In contrast, Deutsch et al. (2014a) in their analysis based on the data of the National Longitudinal Study of Adolescent Health (Add Health) from the USA one year later did not find any influence of the gender composition of the friendship networks on drinking behavior. This confirms the authors' hypothesis that the average alcohol consumption in the peer network has an influence on the alcohol consumption of ego, but this is not dependent on gender (gender) moderated. The authors suspect selection effects here: girls * look for each other accordingly peers, who have a similar drinking behavior as they do themselves. Also for the gender ratio in one peer group No influence on the alcohol consumption of Ego could be proven: Contrary to the assumption, higher proportions of male * adolescents in the network did not result in higher alcohol consumption in either boys * or girls *. Surprisingly, however, the closeness of relationships turned out to be relevant to alcohol consumption: in both boys * (SSF) and girls * (OSF), there was less amicable closeness to male * friends with a stronger influence of these friends on alcohol consumption for one year later along. In this way, however, closeness to girlfriends * only became significant for boys * (OSF). The authors conclude from their findings that the role of gender in socialization with alcohol is much more complex than previously thought, and call for the investigation of a large number of relationships within a network, including those that are less close or non-reciprocal. In addition, the contexts in which adolescents drink and their motives for alcohol consumption need to be examined more closely.
The effect of selection or influencing factors, i. H. The extent to which adolescents choose their peers according to their preferences and needs or are influenced by these in their behavior is investigated in studies with longitudinal data. In many cases, so-called SIENA models are used for this (Simulation Investigation for Empirical Network Analysis) used. The research mainly focuses on the aspects of alcohol, cigarette and cannabis consumption among schoolchildren (e.g. Knecht et al. 2011; Osgood et al. 2013; Pearson et al. 2006). Here are some example studies: With regard to smoking behavior, Finnish secondary school students are more likely to have selection factors that are decisive for friendship. When it comes to alcohol behavior, there are both selection and influencing factors. The results did not consistently differ with regard to gender (Kiuru et al. 2010). Daw et al. (2015) were able to show that boys * like girls * in the USA (7th grade) choose their same-sex friends based on similarity in smoking behavior. An influence of friends * on smoking behavior could only be proven for girls *. With regard to alcohol consumption, Burk et al. (2012) found that the similarity between friends' drinking behavior begins in 6th grade, peaks in 8th grade, and decreases again during late adolescence. Adolescents in all three age groups chose peers with similar drinking behavior, with the effects being strongest in early adolescent men * and late adolescent women *. With regard to the influence, there is no difference between the sexes (Burk et al. 2012). With regard to marijuana use in high schools in the USA, the authors state that the circle of friends is also selected based on age and marijuana use. The influence factor was only found in one high school. However, gender, race, or number of friends outside of school did not significantly predict the frequency of marijuana use. There was also only minimal evidence that peer effects are moderated by personal, school or family risk factors (De La Haye et al. 2013).
Network studies on gender differences and depressive illnesses can also be found for adolescents. Similar to the study by Rosenquist et al. (2011) among adults who came to the conclusion that depression is socially contagious, especially for women *, is shown by Conway et al. (2011) for adolescence that in girls * the occurrence of depression in their circle of friends is accompanied by an increased occurrence of their own depressive symptoms one year later.
Further studies examine very specific network parameters and can show that the same network parameters for girls * and boys * are related to depressive disorders in completely different ways. Boys * are more likely to suffer from depressive illnesses if they are afraid of negative reviews from their peers and are less popular in their network. For girls * who are afraid of negative reviews, this is more the case if they are very popular in their networks (Kornienko and Santos 2014). A study by Falci and McNeely (2009) examines the size and density of networks and shows that girls *, when they are part of very large, fragmented networks (ie, few network members know each other), are more likely than women to be affected by depressive symptoms Girls * who are involved in large but cohesive networks. In the case of boys *, on the other hand, the relationships are exactly the opposite: if they are involved in large and less cohesive networks, they are less affected by depressive symptoms than boys * who are embedded in large and cohesive networks.
Network studies can also be found in the old age. A study of older people (over 60 years of age) in the USA examines the effects of different ideal-type networks (diverse network, network with high social commitment, network with low social commitment and limited network) on wellbeing. Men * who are involved in restricted networks show a particularly low level of well-being. In general, women *, in different network types, rate their health much better than men * (Fiori and Fuller 2017).
Another important health issue in old age is the biographical transition of widowhood. The death of the partner can have negative effects on mental health and lead, for example, to depressive symptoms. The network mechanisms of social support, social engagement and social integration are mentioned in this context as factors that are mentioned above. Alleviate symptoms and have a positive influence on health (see the article: Social networks and health inequalities in old age).In this context there are some indications of relevant gender-specific differences (see Monserud and Wong 2015): Older men * tend to rely on their wives * when it comes to emotional support, housekeeping and maintaining social contacts (see also Lee et al. 2001; Umberson et al. 1992) and women * are more likely to be economically dependent on their husbands * and can therefore be exposed to financial stress if widowed (Arber 2004; Umberson et al. 1992). In addition, only older women * perceive social support as low and only older men * less network involvement is related to poorer self-reported health (Caetano et al. 2013). This could result in different demands on the social relationship networks, which these cannot always meet.
For Mexico, a country in which institutional support systems are less developed and private, family support structures are therefore more important, Monserud and Wong (2015) found in a longitudinal study, for example, that married men * reported fewer depressive symptoms than all other status groups differentiated by sex (married / widowed in wave 1 / widowed in wave 2). However, there were no statistically significant gender differences in terms of depressive symptoms among the recently widowed people (since wave 2). The results on the influence of social support are inconsistent or the effects must be viewed in a differentiated manner: Regardless of marital status, a higher value for emotional support is associated with a lower increase in depressive symptoms, while the receipt of financial or practical support is more pronounced for recently widowed men * than for recently widowed women * - associated with a greater increase in these symptoms. This could be related to the fact that the reliance on this form of support could trigger feelings of dependency, could be associated with the perception of limited autonomy and a reversal of roles in parent-child relationships and thus cause stress (see also the article " Negative aspects of relationships and health inequalities ”). A stronger integration into a social network, which was operationalized through co-residence with children, relatives or friends as well as participation in community activities, has just as different effects to be considered: In general, co-residence with relatives is associated with a higher increase in depressive symptoms , while co-residence with others (children, friends) means a lower increase in depressive symptoms. For recently widowed men * and long-term widowed women *, co-residence with children is associated with a smaller increase in depressive symptoms, while for recently widowed men * co-residence with other people is associated with a greater increase. Social integration in community activities does not generally provide an explanation for the change in depressive symptoms between the two waves. Church attendance was associated with a larger increase in recently widowed women *, while volunteering in community activities was associated with a lower increase in long-widowed women *. There are therefore clear indications that social support and social integration have different meanings for the sexes and that role models and an unequal distribution of tasks in the household and in the partnership play a role here. According to the theory of social capital, social networks also represent a vehicle for social resources for older trans * people, which can be beneficial for successful aging and well-being: “Controlling for background characteristics, network size was positively associated with being female, transgender identity, employment, higher income, having a partner or a child, identity disclosure to a neighbor, engagement in religious activities, and service use. Controlling in addition for network size, network diversity was positively associated with younger age, being female, transgender identity, identity disclosure to a friend, religious activity, and service use "(Erosheva et al. 2016, p. 98).
In summary, the gender category has been studied relatively well compared to the other categories presented in this book. However, the term network is often used as a metaphor rather than a method or theory. The focus is primarily on school class studies and older people.
Studies show inter alia indicate that women * live longer than men *. There are also health differences between the sexes with regard to morbidity. Especially in adolescence, boys * do worse than girls * on most health-related indicators (e.g. leukemia, epilepsy, chronic diseases). During puberty, girls * seem to be more likely to suffer from psychosomatic and physiological complaints. From this point on, the female * disease profile is more characterized by chronic diseases as well as psychosomatic and psychological impairments (e.g. thyroid diseases, depression, eating disorders); the male *, on the other hand, through acute and life-threatening diseases (e.g. HIV infection, malignant neoplasms of the digestive organs as well as the lungs and bronchi). In old age, there is hardly any general gender difference to be seen. With regard to risk behavior, research shows that men * both smoke more and consume more alcohol than women *. While men * rarely make use of psychiatric, psychotherapeutic and outpatient medical services into adulthood, this level out again in old age.
Network studies identify differences between men * and women *. It can be said that women * have larger networks, which in turn have more family and family diversity. However, recent studies assume that the networks of both sexes are slowly converging. Looking at the resources gained from social relationships, there is evidence that help in the event of illness is more often provided by women *. Mothers * also take on the more time-consuming support, and women * seem to have more contact persons for problems than men *. On the other hand, whoever is preferred when seeking help, men * or women *, seems contradictory, although more studies show a tendency towards female * helpers. The picture for professional network relationships is as follows: Men * have larger work-related networks, they are connected to other sub-networks and derive more advantages from these relationships, since men * occupy higher positions in professional networks. Women * seem to focus more on their individual competencies rather than on social capital, while men * rely more on networks and make better use of resources.
Studies on the connection between networks and social capital or social support against the background of health inequalities show an unequal distribution between the sexes. Women * seem to take on more and more time-consuming social support tasks. They have more contact persons for problems than men *. In addition, they probably suffer more often from negative aspects of social relationships. It is assumed that women * are exposed to higher health burdens than men * due to their greater social commitment.
In general, the health and health behavior of schoolchildren and older people are the focus of network research. Network studies often examine cigarette, alcohol and cannabis use among adolescents and adolescents. In addition to the cross-sectional studies, more recent longitudinal studies investigate the influencing and selection factors. They investigate the extent to which young people choose their friends based on their preferences and needs or are influenced by them in their behavior. Here the research situation seems to be rather heterogeneous, perhaps also due to the different data sets and country focus. However, it becomes clear that girls * or young women * consume less light drugs than their male * peers and that social networks have a major effect on health behavior. To what extent there are gender differences in the network effects remains to be seen. There is also a connection between depressive illnesses and social networks, which apparently is subject to gender-specific factors.
In old age, the main focus is on the widowhood phase and the associated network effects. It seems that networks have a positive impact on health. Nevertheless, there are also negative sides to networks. There are indications of gender differences here. For example, men * lose emotional support and parts of their social contacts through the death of their wife *, while women * can be exposed to financial stress through their economic dependence on their husband *.
The very few studies on trans * people show that many trans * people live on the margins of society and are confronted with stigmatization, discrimination, exclusion, violence and poor health (Winter et al. 2016).
As a conclusion, we would like to briefly address the desiderata. Despite a number of scientific studies, many questions remain unanswered. Above all, we want to point out that intersex people11 is (almost) never taken into account in the studies. Furthermore, network research has so far played a subordinate role in the analysis of the benefits of social support or social capital. But also specific questions are hardly taken into account. While the influence of networks on risk behavior has already been very well researched, the question arises as to which positive aspects social networks have on health behavior, e.g. B. exercising or renouncing certain drugs. Even with the explanatory models regarding the illness and the course of illnesses, the question of the effects of class, gender and social network connections should be given even greater focus, not only with newer methods of quantitative but also qualitative network research. In addition, it would be important here to combine the concept of intersectionality even better with network research.
Kolip, P. & Hurrelmann, K. (2016). Handbook Gender and Health: Men and Women in Comparison. Bern: Hogrefe. The handbook offers an up-to-date and comprehensive overview of the state of scientific research and interdisciplinary discourses in the context of gender and health.
Barry, A.-M. & Yuill, C. (2012). Understanding the sociology of health: An introduction. 3rd ed. Los Angeles, CA: Sage. The section on Gender and Health (p.129–144) offers readers an easily understandable introduction to the relationship between health and gender based on international data.
Bradford, J., Reisner, S.L., Honnold J.A. & Xavier, J. (2013). Lesbian, gay, bisexual, and transgender health: Findings and concerns. Journal of the Gay and Lesbian Medical Association, 4 (3), Pp. 102-151. A dense and good English overview article on the subject of LGBT and health.
Moore, G. (1990). Structural determinants of men’s and women’s personal networks. American Sociological Review, 55 (5), Pp. 726-735. Older but exemplary representative study from the USA using quantitative data from the General Social Survey (GSS, 1985) on strong relationships.
Schwartz, E. & Litwin, H. (2018). Social network changes among older Europeans: The role of gender. European Journal of Aging, 15 (4), pp. 359-367. Current, quantitative longitudinal study using the Europe-wide Survey of Health, Aging, and Retirement in Europe (n = 13,938) asks about gender differences in the social networks of older people (65+).
With the use of the "*", the authors indicate on the one hand the constructive nature of the gender category and on the other hand they want to show that gender identity and gender expression are not closed categories, but also beyond the binary of female * and male * or woman * and man *, diversity exists and there are people who do not want or cannot clearly assign themselves to the dualistic gender order. There is now a growing number of studies specifically on trans * people, but gender is queried as a dichotomous variable (“male” / “female”) in the majority of general studies (Döring 2013). In this article, we focus on studies with dichotomous gender variables, but if possible also include findings from research on trans * people. On the other hand, the "*" indicates that when designating groups of people, both female and male persons are expressly meant, as well as all people who do not want or cannot clearly assign themselves to the dualistic gender order.
The (self) designation trans * is a generic term for various designations beginning with “trans”, for example transgender, transidentity and transsexuality etc. In addition, the “*” takes on the role of a placeholder for diverse (self-) ) Names (Gerede e.V. 2018).
Queer primarily describes people who do not identify with traditional gender roles and stereotypes and who question alleged bisexuality. Queer also stands for people who, through their self-definition (trans *, multisexual, lesbian, etc.), reject heteronormativity (i-Päd 2019).
For the years 2015–2020, the United Nations was able to determine the following exemplary life expectancies, measured in years: Afghanistan (men * (M *): 62.7; women * (F *): 65.6); Brazil (M *: 72.2; F *: 79.4); Japan (M *: 80.7; F *: 87.1); Canada (M *: 80.7; F *: 84.4); Estonia (M *: 73.01; F *: 81.9); Kenya (M *: 64.9; F *: 69.6); New Zealand (M *: 80.4; F *: 83.7) (UNdata 2017).
There are also gender differences in the type of malignant neoplasms. According to the Federal Statistical Office (2019i), lung and bronchial cancer can be listed as the most common cancer causing death for the male * genders in 2016, with 29,305 of 125,128 cases. In women * it is 18,570 of 105,597 cases of breast cancer.
People who identify as predominantly male, but who were assigned to the female gender at birth (Gerede e.V. 2018).
These viruses can infect the skin and various mucous membranes and cause uncontrolled tumor-like growth.
Gender-specific differences in connection with risk factors exist in the influence of diabetes mellitus, which is more pronounced in women * than in men *. According to Regitz-Zagrosek (2017), diabetes mellitus increases the incidence of CHD in women * by 5 to 7 times and in men * by 3 to 4 times. The classic risk factors such as hypertension, cigarette smoking, and hypercholesterolemia, on the other hand, have a similar effect on the CHD risk in women * as in men *.
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