Risk behaviors and resiliency within physically abused adolescents

Child Abuse & Neglect 28 (2004) 547–563

Risk behaviors and resiliency within physically abused adolescents夽 Daniel F. Perkins∗ , Kenneth R. Jones Family and Youth Policy for Resiliency, Department of Agricultural and Extension Education, The Pennsylvania State University, 323 Agricultural Administration Building, University Park, PA 16802, USA Received 26 February 2003; received in revised form 1 December 2003; accepted 19 December 2003

Abstract Objective: This study examines the relationship between physical abuse and several risk behaviors, and thriving behaviors, and the relationship between potential protective factors and engagement in risk and thriving behaviors among victims of physical abuse. Three categories of potential protective factors were examined: (1) individual characteristics, (2) family processes, and (3) extra-familial factors. We expected that high levels of protective factors would reduce engagement in risk behaviors (i.e., alcohol use, tobacco use, drug use, sexual activity, antisocial behavior, attempted suicide, and purging) among abused adolescents. Results: Across all the risk behaviors, abused adolescents reported a higher frequency of engagement than non-abused adolescents. Several protective factors were identified for the seven risk behaviors. Peer group characteristics was a significant predictor in all seven of the logistic regressions, followed by positive school climate (six models), religiosity (five models), other adult support (five models), family support (four models), view of the future (two models), and involvement in extra-curricular activities (two models). The variance accounted for by the models ranged from 2% (risk behavior of purging) to 26% (risk behavior of alcohol use and antisocial behavior). Conclusions: The findings indicate that, with the exception of sexual activity, the majority of abused adolescents were not engaging in risk behaviors; however, significantly more abused adolescents were engaging in risk behaviors than their non-abused counterparts. In addition, that protective factors were found to exist at various levels of the adolescents’ ecology has strong implications for practice. © 2004 Elsevier Ltd. All rights reserved. Keywords: Physical abuse; Protective factors; Risk behaviors; Resiliency

夽 ∗

Support was provided by Penn State University’s Agricultural Experiment Station and Cooperative Extension Service. Corresponding author.

0145-2134/$ – see front matter © 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.chiabu.2003.12.001

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Introduction Resiliency can only be displayed or detected through an individual’s response to adversity, whether it is a stressful life event or a situation of continuous stress (e.g., war, abuse) (Masten & Coatsworth, 1998). Accordingly, resilient people are well-adapted individuals in spite of serious stressors in their lives (Luthar, 1991; Masten, 2001). Human adaptation or competence is composed of the interplay between the context/ecology and the developing organism (Lerner, 1995; Schneirla, 1957). Moreover, resiliency, itself, is multidimensional in nature. Thus, one may be resilient in one domain but not exhibit resiliency in another domain. As Luthar and her colleagues (Luthar, Cicchetti, & Becker, 2000) stated, “Some high-risk children manifest competence in some domains but exhibit problems in other areas” (p. 548). In a study by Kaufman and colleagues (Kaufman, Cook, Arny, Jones, & Pittinsky, 1994), for example, approximately two-thirds of children with histories of maltreatment were academically resilient; however, when examining these same children in the domain of social competence, only 21% exhibited resiliency. This study explores resiliency in adolescents who reported being physically abused. Specifically, the study examines the relationship between physical abuse and several risk behaviors, as well as the relationship between physical abuse and thriving behaviors. Thriving behaviors are prosocial behaviors (e.g., helping others) and behaviors needed by adolescents to competently contribute to a civil society (Lerner & Benson, 2003; Scales, Benson, Leffert, & Blyth, 2000). The examination of multiple domains of risk and thriving behaviors is the unique contribution of this study. Indeed, no study of resiliency has examined nine domains simultaneously—seven risk behaviors and two thriving behaviors. The key questions addressed in this study are: (1) How prevalent is physical abuse among various groups of adolescents defined by gender, ethnicity, family structure, and SES? (2) What proportions of adolescents who have been physically abused are exhibiting problem behaviors and what proportions are exhibiting thriving behaviors? and (3) What protective factors are associated with a reduced likelihood of engagement in problem behaviors and increased thriving behaviors among victims of physical abuse? Physical abuse as a risk factor The assumption of resiliency research is that resiliency can only be displayed or detected through an individual’s response to a stressful life event or situation; the current investigation employed child abuse as its stressful life event or situation. Child abuse has been found to have significant adverse effects on the development and adjustment of children, adolescents, and adults (Garmezy, 1985; Trickett & McBride-Chang, 1995). Indeed, according to Masten and Coatsworth (1998), abuse is a situation of continuous stress and similar to war is a overwhelming stressful experience that has been linked to long term maladaptive behaviors. Moreover, a history of sexual and/or physical abuse has also been found to be associated with adolescent engagement in sexual activity (e.g., Benson & Roehlkepartain, 1993; Perkins, Luster, Villarruel, & Small, 1998). Therefore, child abuse meets the definition of a stressful life event. Protective factors Three categories of potential protective factors were examined: (1) individual characteristics, (2) family processes, and (3) extra-familial factors. In their extensive review of 25 years of resiliency research, Masten and Coatsworth (1998) identified protective factors related to the well-being of youth. At the individual level, the current investigation utilizes religiosity and view of the future as potential protective factors asso-

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ciated with resiliency. For example, several researchers found that religiosity provides adolescents with a sense of purpose; that is, despite hard times, some adolescents draw on their faith for a sense of confidence that things will work out (Dryfoos, 1990; Hawkins, Jenson, Catalano, & Lishner, 1988; Hawley & DeHaan, 1996; Higgins, 1988a, 1988b; Luthar & Zigler, 1991; Perkins, Luster, & Jank, 2002; Werner, 1990). Several studies also found that family processes function as protective factors (Block, 1971; Masten et al., 1988; Rutter, 1979; Werner & Smith, 1982, 1992). For example, Rutter (1979) found that a good relationship with at least one parental figure could protect against the risk associated with family discord. Moreover, Blum and his colleagues (Blum, Beuhring, & Rinehart, 2000) found that the presence of a positive parent-family relationship was a consistent protective factor that cut across several risk behaviors. For this study, we examined two potential protective factors in the family domain: (a) the amount of family support the adolescent perceived that he or she received, and (b) the amount of positive communication between the adolescent and parent(s), as perceived by the adolescent. Protective factors also have been identified at the extra-familial level of the adolescent’s ecology and include: supportive relationship with adults other than the adolescent’s parents (Luthar & Zigler, 1991; Werner, 1990), a positive school climate (Blum, McNeely, & Rinehart, 2002; Bogenschneider, Wu, Raffaelli, & Tsay, 1998; Rutter, 1987), peer group characteristics (Blum et al., 2000; Hawkins et al., 2000; Mahoney & Stättin, 2000; Perry, 2000), and involvement in extra-curricular activities (National Research Council and Institute of Medicine, 2002). For instance, Benson (1990) suggested that having support from an adult outside the family is an asset for adolescents that decreases their likelihood of engaging in risk behaviors. Resiliency researchers found that a supportive adult outside the family helps adolescents to feel a sense of coherence and optimism in an otherwise troubled environment (Hawley & DeHaan, 1996; Werner & Smith, 1982, 1992). However, Perkins and his colleagues (Perkins et al., 2002) found that having a close relationship with adults outside the home was linked to abused adolescent females engaging in the behavior of purging. A second extra-familial influence we examined was a positive school climate. Rutter and Quinton (1984) and Blum et al. (2000) found a relationship between positive school experiences and favorable outcomes in adulthood (e.g., good parenting) among women who had been raised in institutions as children. More recently, Blum and his colleagues (Blum et al., 2002) found that adolescents who were connected to their school were less likely to: use alcohol and drugs, engage in deviant behaviors, become pregnant, and experience emotional stress. Positive experiences in school may be especially important if adolescents experience adversity at home (e.g., abuse or non-caring parent). Based on these previous studies, we expected to find that those adolescents who had been physically abused would be less likely to be involved in risk behaviors if they: (a) were involved in religion, (b) had a positive view of the future, (c) had supportive relationships with family members, (d) had open lines of communication with their parents, (e) had peers engaged in positive behaviors, (f) had supportive relationships with adults outside the family, (g) had positive experiences in school, and (h) were involved in extra-curricular activities. Methods Participants A sample of 16,313 adolescents, between the ages of 12 and 17, was drawn from a large Midwestern state (Keith & Perkins, 1995). Initially, a random sample of schools was drawn from a list

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of public schools in Michigan. An insufficient number of schools from the random sample (i.e., 9 of the sample of 40 schools) were willing to participate in this study, but many other schools volunteered to participate. Overall, this study involved 43 middle and high schools in 36 communities. The study focused primarily on seventh, ninth, and eleventh graders. However, one quarter of the participating schools collected information from all middle and high school students. Public school participation was solicited at the school or district level by one of three people: the county 4-H youth development extension educator, the county family and consumer science extension educator, or research staff. Measures Expert criterion validity was employed in the creation of the scales. Ten raters were asked to assign each of the items to one of the scales. The raters were given the definitions of the scales (constructs) and were asked to place each item into the category they believed was most associated with the content of the item. To maximize the level of external rater validity obtained by the use of the expert raters, it was decided that a minimum level of 80% agreement among raters would be used as the criterion for placement of an item into a category. Several potentially related factors were identified from the larger set of variables measured in the study in a comprehensive review of the literature regarding resiliency and physical abuse. The risk factor, physical abuse, is presented, followed by the demographic variables, protective factors, risk behaviors and thriving behaviors. Physical abuse. Physical abuse was measured by one item, “Have you ever been physically abused by an adult (i.e., where an adult caused you to have a scar, black and blue marks, welts, bleeding, or a broken bone)?” The range of choices was “1” (never), “2” (once), “3” (2–3 times), “4” (4–10 times), and “5” (more than 10 times). The item was collapsed into a dichotomous variable, resulting into two choices: “0” (never) and “1” (one or more times). Demographic variables Ethnicity. Five ethnic groups were examined in this study: African American, Asian or Pacific Islander, European American, Hispanic, and Native American. Family structure. Two items addressed family structure: “Do you live most of the time in a family with two parents?” and “Do you live all or most of the time in a single-parent family?” Both items were combined to create a single index. Socioeconomic status (SES). Parents’ education was used as an indicator to determine the SES of the adolescents. The first item was: “What is the highest level of schooling your father completed?” The second item was the same, except that it addressed the mother’s education level. There were six response choices ranging from “completed grade school or less” to “graduate or professional school after college.” The item responses were added to create the single index “SES,” with the highest level of education obtained by either parent the variable employed to indicate whether an adolescent was at the high or low SES level.

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Protective factors Religiosity. Adolescents’ religiosity was indexed by three items that pertained to attendance at religious services and views on the importance of religion in their lives. The first item was: “How often do you attend religious services at a church or synagogue?” The range of choices was “1” (never) to “4” (about once a week). The second item was: “During an average week, how many hours do you attend services, groups, or programs at a church or synagogue?” Possible responses were: “1” (0 hours), “2” (1–2 hours), “3” (3–5 hours), “4” (6–10 hours), and “5” (11 hours or more). The third item asked adolescents about their view of religion: “How important is religion in your life?” For this item, there were four possible responses, ranging from “1” (not important) to “4” (very important). Cronbach’s alpha for religiosity was .79. View of the future. Three items comprised the index for adolescents’ view of the future. The items, “I worry a lot about my future,” “Ten years from now, I think I will be happy,” and “When I am an adult, I think I will be successful in whatever work I choose to do” were on a scale from “1” (strongly agree) to “5” (strongly disagree). Two of the items were reversed so that high scores indicated a more positive view of the future. Cronbach’s alpha for view of the future was .16. Because expert rater agreement for these items was 100%, the authors decide to keep this scale despite the low Cronbach’s. Family support. Adolescents’ level of family support was derived from a four-item scale. These items were: “My family life is happy,” “There is a lot of love in my family,” “My parents give me help and support when I need it,” and “My parents often tell me they love me.” All items from this scale were scored on a five-point Likert scale, with responses ranging from “1” (strongly disagree) to “5” (strongly agree). Cronbach’s alpha for family support was .82. Parent-adolescent communication. Four items from the Search Institute’s Profiles of Student Life: Attitude and Behavior Questionnaire (ABQ) assessed the communication between parents and adolescents. Two of the items were scored using a five-point Likert scale ranging from “1” (strongly agree) to “5” (strongly disagree). The two items were: “I have lots of good conversations with my parents,” and “My parents are easy to talk with.” The third item was, “If you had an important concern about drugs, alcohol, sex, or some serious issues, would you talk to your parent(s) about it?” The five-point scale was “1” (yes), “2” (probably), “3” (I’m not sure), “4” (probably not), and “5” (no). The fourth item asked, “How many times in the last month have you had a good conversation with one of your parents that lasted 10 minutes or more?” The choices ranged from “1” (none) to “5” (4 or more times). The mean of the responses to the items was calculated after the scores had been standardized. The Cronbach’s alpha for parent-adolescent communication was .85. Peer group characteristics. Five items were employed to create the index for the variable “peer group.” The following items were included: “Among the people you consider to be your closest friends, how many would you say (a) drink alcohol once a week or more? (b) have used drugs such as marijuana or cocaine? (c) get into trouble at school? (d) do well in school? and (e) help other people?” The choices were: “1” (none), “2” (a few), “3” (some), “4” (most), and “5” (all). Cronbach’s alpha for peer group characteristics was .67.

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Other adult support. Two items from the ABQ assessed adolescents’ perceived support from other adults besides their parents. Those items were: “How many times in the last month have you had a good conversation with an adult (other than a parent) that lasted 10 minutes?” and “If you had an important question about your life, how many adults do you know (not counting your parents) to whom you would feel comfortable going for help?” The range of choices for the former question was “1” (never), “2” (once), “3” (twice), “4” (3 times), and “5” (4 or more times). The possible responses for the latter question were “1” (none), “2” (1), “3” (2), “4” (3 to 4), and “5” (5 or more). Cronbach’s alpha for other adult support was .57. School climate. Adolescents’ perceptions of school climate were assessed by adding four items: “My teachers really care about me,” “My teachers don’t pay much attention to me,” “I get a lot of encouragement at my school,” and “I like school.” The items used a five-point scale ranged from “strongly agree” to “strongly disagree.” Cronbach’s alpha for school climate was .70. Involvement in extra-curricular activities. Four items were used to create an index for “Involvement in extra-curricular activities.” The items included: “During an average week, how many hours do you spend (a) in band, choir, orchestra, music lessons, or practicing voice or an instrument? (b) in clubs or organizations outside of school? (c) in clubs or organizations at school (other than sports)? and (d) playing sports on a school team?” Each item was on a five-point scale: “1” (0 hours), “2” (1–2 hours), “3” (3–5 hours), “4” (6–10 hours), and “5” (11 or more hours). Cronbach’s alpha for extra-curricular activities was .35. We did not expect a high Cronbach’s alpha as there is no reason to expect that an individual involved in one type of activity would be involved in another type of activity. However, we did use the scale because a combination of the items affords a broad measure of involvement in extra-curricular activities. Risk behaviors Problem alcohol use. Two items indexed problem alcohol use. The first item, “How many times, if any, have you had alcohol to drink during the last 30 days?” was on the following scale: “1” (0), “2” (1–2), “3” (3–5), “4” (6–9), “5” (10–19), “6” (20–39), and “7” (40+). The second item, “Think back over the last 2 weeks. How many times have you had five or more drinks in a row? (A “drink” is a glass of wine, a bottle or can of beer, a shot glass of liquor, or a mixed drink),” was on the following scale: “1” (none), “2” (once), “3” (twice), “4” (3–5 times), “5” (6–9 times), and “6” (10 or more times). The two separate items were recoded into the following scale: “1” (none), “2” (1–2), “3” (3–5), “4” (6–9), and “5” (10+). The new variable “alcohol use” was converted into a dichotomous variable, with the two categories: “0” (none to 1–2 drinks within the last 30 days), and “1” (3–5 drinks or more within the last 30 days). Cronbach’s alpha for alcohol use was .84. Tobacco use. Two items were used to determine tobacco use (cigarette smoking). The first item, “How many times, if any, have you smoked cigarettes during the last 30 days?” was on the scale: “1” (0), “2” (1–2), “3” (3–5), “4” (6–9), “5” (10–19), “6” (20–39), and “7” (40+). The second item, “During the last 2 weeks, about how many cigarettes have you smoked?” used: “1” (none), “2” (less than 1 cigarette per day), “3” (1 to 5 cigarettes per day), “4” (about 2 pack per day), “5” (about 1 pack per day), “6” (about 12 packs per day), and “7” (2 packs or more per day). The items were added to create an index and then

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recoded. The new variable “tobacco use” was converted into a dichotomous variable with the following scale: “0” (none to a maximum of a pack per month), and “1” (at least 1–5 cigarettes per day; a pack per week). Cronbach’s alpha for tobacco use was .79. Drug use. Drug use (marijuana, cocaine, and/or amphetamines) was composed of a three-item index: “How many times, if any, have you used marijuana (grass, pot) or hashish (hash, hash oil) during the last 30 days?” Similar questions asked about cocaine use (crack, coke, snow, rock) and amphetamine use. The new variable “drug use” was converted into a dichotomous variable with the following scale: “0” (none = none within past 30 days), and “1” (drug use = at least once within the past 30 days). Cronbach’s alpha for drug use was .81. Sexual activity. Sexual activity was measured with a single item, “Have you ever had sexual intercourse (“gone all the way,” “made love”)” on the scale: “1” (no), “2” (once), “3” (twice), “4” (3 times), and “5” (4 or more times). The existing item was recoded into the new variable “sexual activity,” and contained the following responses: “0” (no) and “1” (yes). Suicide. The index for suicide involved a single item, “Have you ever tried to kill yourself?” on the following scale: “1” (no), “2” (yes, once), “3” (yes, twice), and “4” (yes, more than two times). The item was recoded to create the dichotomous index variable: “0” (no) and “1” (yes). Antisocial behavior and delinquency. The antisocial behavior and delinquency index was composed of six items: “During the last 12 months, how many times have you (a) stolen from a store? (b) damaged property just for fun (such as breaking windows, scratching a car, putting paint on walls, etc.)? (c) used a knife or a gun or some other thing (like a club) to get something from a person? (d) gotten into trouble with the police? (e) hit or beat up someone? and (f) hurt someone badly enough to need bandages or a doctor?” The five-point scale consisted of the following choices: “1” (never), “2” (once), “3” (twice), “4” (3–4 times), and “5” (5 or more times). Once the items were combined into the new variable “antisocial,” the scale was converted to a dichotomous variable, with the choices: “0” (none to a maximum of once within the past year) and “1” (at least twice within the past year). Cronbach’s alpha for antisocial behavior was .76. Purging. Purging was measured by one item, which asked respondents, “How often do you vomit (throw up) on purpose after eating?” The range of responses was “1” (never), “2” (once a month or less), “3” (2–3 times a month), “4” (once a week), and “5” (2 or more times a week). For analysis purposes, the item was converted into a dichotomous variable with the following response choices: “0” (never to once a month) and “1” (at least 2–3 times per month). This cutoff was based on DSM-IV criteria of bulimia—those who purge themselves two or more times per week. Thriving behaviors Success in school. Success in school was measured by the item, “What kinds of grades do you earn in school?” The possible choices were: “1” (mostly A), “2” (about half A and half B), “3” (mostly B), “4” (about half B and half C), “5” (mostly C), “6” (about half C and half D), “7” (mostly D), “8” (mostly

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below D). The variable was recoded into a new variable, “school success,” having two levels: about half B/half C and below = low, or mostly A to mostly B = high. Helping others. Three items were combined to create the helping others index. The variable consisted of the following items: “During the last 12 months, how many times have you (a) been involved in a project to help make life better for other people? (b) given money or time to a charity or organization that helps people? and (c) spent time helping people who are poor, hungry, sick, or unable to care for themselves?” There were five response choices for the three items: “1” (never), “2” (once), “3” (twice), “4” (3–4 times), and “5” (5 or more times). The variable was recoded into levels: “0” (low = never to once within the past year), and “1” (high = at least twice within the past year). Cronbach’s alpha for helping others was .72. Procedure Data were collected via self-report surveys administered by classroom teachers, either in fall 1993, or winter 1994. The survey was Search Institute’s Profiles of Student Life: Attitude and Behavior Questionnaire (ABQ), a 152-item inventory developed by the Search Institute (Benson, 1990; Blyth, 1993). Participation rates among the school were quite high (i.e., 92–100%) with only absentees not completing the survey. Teachers administered the questionnaire by following a specific script and an instruction manual from the Search Institute. All of the participants, within their respective schools, were administered the questionnaire in a classroom setting during one specific time during the school day. The survey was administered to participants with the assurance of anonymity. Internal Review Board approved the policy that each school determined whether written consent or passive consent of parents was required before students could participate in the survey. Students placed completed surveys in an envelope, which was sealed and mailed to the project staff. Data analysis In order to examine resiliency, the majority of the analyses involved a sub-sample of adolescents. The sub-sample was composed of the 3,281 adolescents who reported being physically abused one or more times in the past. Thus, in our final analysis, we compared physically abused adolescents who were exhibiting risk behaviors and physically abused adolescents who were exhibiting thriving behaviors. This sub-sample of represented approximately 20% of the overall adolescent sample (mean age 14.5 years, SD = 1.6). Several analyses were conducted to address the questions of this study. Nine logistic regressions were used to fit the models—one for each risk behavior as the dependent variable (seven models), and one for each of the thriving behaviors (two models). The logistic regression equations consisted of the following independent variables: religiosity; parent-adolescent communication; family support; view of the future; other adult support; peer group characteristics; school climate; and involvement in extra-curricular activities. Logistic regression was employed as the analysis because the dependent variables were dichotomous, and this analysis yields a probability of the event, that is, the probability is transformed into an odds ratio (Vogt, 1993). A full model with main effects was fitted, and backwards elimination was used to obtain a reduced model. The Nagelkerke R-Square estimate was used to determine the percentage of variance explained by the independent variables included in each

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Table 1 Sample characteristics of adolescents who reported being abused

Total Gender Male Female

Sample size

Percent of total sample

Percent of sample who were abused

Abuse sub-sample (used in analyses)

16,313

100

20.1

100 (3,281)

7,618 8,695

46.7 53.3

7.7 12.4

38.4 (1,260) 61.6 (2,021)

Ethnicity African American Asian European American Hispanic Native American

3,931 163 11,175 440 604

24.1 1.0 68.5 2.7 3.7

4.7 .2 13.3 .5 1.2

23.8 (781) 1.1 (36) 66.6 (2,185) 2.7 (89) 5.8 (190)

Family structure Single-parent Two parent

4,551 11,762

27.9 72.1

7.0 12.9

35.2 (1,155) 64.8 (2,126)

9,608 6,705

58.9 41.1

13.0 7.0

64.9 (2,129) 35.1 (1,152)

SES Low High

Note. Contingency coefficients: gender = .08, p < .001; ethnicity = .057, p < .001; family structure = .080, p < .001; socio-economic status = .061; p < .001.

equation. A conservative estimate of significance (p < .01) was employed because of the large sample size.

Results First, prevalence of physical abuse among various groups of adolescents was examined in terms of gender, ethnicity, family structure, and SES. Out of the 16,313 adolescents who completed the survey, 46.7% were males (see Table 1). The majority of the respondents were White (68.5%), followed by African Americans (24.1%), Native Americans (3.7%), Hispanics (2.7%), and Asians (1%). Most adolescents came from two-parent households (72.1%). The distribution of adolescents coming from different SES backgrounds ranged from a low SES background (58.9%). Of the 20% of the total sample that had been abused, 8% were males and 12% were females. The sub-sample employed for the current study included only those adolescents who reported being physically abused (20.1%). Females comprised the majority of this sub-sample (61.6%). Ethnic distribution of the sub-sample mirrored the overall sample. Proportions of physically abused adolescents exhibiting problem behaviors and thriving behaviors Across all the risk behaviors, physically abused adolescents reported a higher frequency of engagement in problem behaviors than non-abused adolescents (see Table 2). For example, 36% of those adolescents

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Table 2 Percentages of non-abused adolescents and abused adolescents who reported high levels of risk behaviors Percent of non-abused (N)

Percent of abused (N)

Total

79.9 (13,032)

20.1 (3,281)

Variables Alcohol use Tobacco use Drug use Sexual activity Antisocial behavior Suicide Purging School success Helping others

22.2 (2,893) 10.2 (1,329) 14.6 (1,903) 40.2 (5,239) 6.9 (899) 10.0 (1,303) 3.1 (404) 57.0 (7,428) 28.7 (3,740)

36.2 (1,188) 23.0 (755) 28.9 (948) 58.4 (1,916) 14.0 (459) 31.5 (1,034) 8.9 (292) 46.7 (1,532) 32.0 (1,050)

∗

Contingency coefficient∗

.128 .152 .149 .146 .104 .238 .114 .083 .029

p < .001.

who were physically abused reported a high use of alcohol, which was defined as a minimum of 5 to 10 drinks within the last 2 weeks. In comparison, approximately 22% of those adolescents who were not abused reported a high use of alcohol. Moreover, the frequent use of drugs, that is, using drugs (i.e., marijuana, amphetamines, or crack/cocaine) once within the last 30 days, was reported by 29% of physically abused adolescents. Fewer non-abused adolescents reported the use of drugs at high level (15%). Some abused adolescents reported engaging in thriving behaviors, although not as many as their non-abused counterparts. Of the total number of abused adolescents, approximately 47% reported having mostly “A”s and some “B”s in school. In comparison, 57% of the non-abused adolescents received mostly “A”s and some “B”s. More than one-third of abused adolescents (36%) reported receiving average grades of C, and 17.2% were classified as below average. In comparison, 32% of the non-abused adolescents reported average grades, while 11% indicated below average grades. Approximately a third (32%) of abused adolescents indicated a high willingness to help others, compared to 29% of non-abused adolescents. Protective factors associated with a reduced likelihood of problem behaviors and increased thriving behaviors among abused adolescents Five factors were found to be significant predictors in the logistic regression for alcohol use. They included: religiosity, family support, other adult support, peer group characteristics, and school climate (see Table 3). Abused adolescents who reported religiosity and family support were less likely to report heavy drinking (i.e., a minimum of 5–10 alcoholic drinks within the last 2 weeks) than abused adolescents who reported lower religiosity. Having support from other adults was related to increased alcohol use among abused adolescents. For example, abused adolescents with other adult support were 1.5 times more likely to engage in drinking alcohol. Abused adolescents who perceived that their peers were engaging in positive behaviors and not in negative behaviors were more likely to report lower levels of drinking than abused adolescents who indicated that their peers were engaged in risk behaviors and not in positive behaviors. Abused adolescents who reported a more positive school climate were less likely to drink than

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Table 3 Significant predictors from the logistic regressions of individual risk and thriving behaviors of abused adolescents β

SE

Wald

df

p

Odds ratioa

−.31 −.13 .38 −1.15

.05 .05 .05 .07

35.51 8.28 65.07 313.74

1 1 1 1

.001 .01 .001 .001

.73 .88 1.47 .32

−.26

.06

22.10

1

.001

.77

−.41 −.16 .34 −1.01

.06 .05 .05 .07

42.45 9.83 42.10 218.70

1 1 1 1

.001 .01 .001 .001

.67 .86 1.41 .36

−.24 −.27

.06 .08

14.63 10.05

1 1

.01 .01

.79 .77

−.26 .31 −1.13

.05 .05 .07

22.50 39.96 294.13

1 1 1

.001 .001 .001

.77 1.36 .32

Characteristics School climate

−.21

.06

13.80

1

.001

.81

Sexual activity (N = 2,889) Religiosity View of future Adult support Peer group

−.30 .20 .33 −.89

.05 .07 .05 .06

37.97 8.43 54.16 205.16

1 1 1 1

.001 .01 .001 .001

.74 1.23 1.39 .41

Characteristics School climate

−.22

.05

18.49

1

.001

.80

−.25 −1.28

.08 .08

10.60 230.28

1 1

.001 .001

.78 .28

Characteristics School climate Involvement in extra-curricular activities

−.48 .35

.07 .10

41.54 13.06

1 1

.001 .001

.62 1.42

Suicide (N = 3,012) Family support View of future Adult support Peer group

−.40 −.24 .12 −.37

.04 .07 .04 .06

85.73 12.16 7.36 44.14

1 1 1 1

.001 .001 .01 .001

.67 .79 1.13 .69

Risk behavior variables Alcohol use (N = 3,002) Religiosity Family support Other adult support Peer group Characteristics School climate Tobacco use (N = 3,001) Religiosity Family support Other adult support Peer group Characteristics School climate Involvement in extra-curricular activities Drug use (N = 2,999) Religiosity Adult support Peer group

Antisocial behavior (N = 2,983) Religiosity Peer group

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Table 3 (Continued ) β

SE

Wald

df

p

Odds ratioa

Characteristics School climate

−.20

.05

13.31

1

.001

.82

Purging (N = 3,008) Family support Peer group

−.20 −.27

.06 .09

9.89 9.83

1 1

.01 .01

.82 .77

Characteristics Involvement in extra-curricular activities

−.26

.10

6.85

1

.01

1.29

−.128 .48 .35 .66

.04 .06 .05 .06

9.37 74.37 48.13 105.05

1 1 1 1

.01 .001 .001 .001

.88 1.61 1.41 1.93

Helping others (N = 3,004) Religiosity Other adult support Peer group

.42 .31 .35

.05 .05 .06

71.12 45.17 35.89

1 1 1

.001 .001 .001

1.52 1.37 1.42

Characteristics Involvement in extra-curricular activities

.62

.07

87.41

1

.001

1.86

Thriving behavior variables School success (N = 3,001) Other adult support Peer group School climate Involvement in extra-curricular activities

a

The reference group for the odds ratio is used for comparison. For this analysis it was European American females.

those who reported a less positive school climate. These independent variables collectively explained 26% of the variance for the problem behavior of alcohol use. For the problem behavior of tobacco use, six factors were found to be significant predictors: religiosity, family support, other adult support, peer group characteristics, school climate, and involvement in extra-curricular activities. Five of the six factors (all except other adult support) were negatively related to at least one pack of cigarettes per month. For example, as religiosity increased, tobacco use decreased in abused adolescents. However, support from other adults was positively related to tobacco use. Thus, those abused adolescents who reported strong support from other adults were more likely to use tobacco than those abused adolescents who reported little support from other adults. This model explained 24% of the variance. The third logistic regression equation examined drug use (e.g., marijuana, amphetamines, or crack/ cocaine). Four factors were found to be significant in this model. Religiosity peer group characteristics, and school climate were negatively related to drug use. For example, as religiosity increased, reports of drug use decreased in abused adolescents. However, support from other adults was positively related to drug use. According to the odds ratio, abused adolescents who reported support from other adults were 1.4 times more likely to use drugs than abused adolescents who reported lower levels of support from other adults. This model explained 23% of the variance. The fourth logistic regression equation examined sexual activity; five factors were significant in the equation religiosity, peer group characteristics, and positive school climate had an inverse relationship with sexual activity for abused adolescents. However, abused adolescents who reported strong support

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from other adults were more likely to be sexually active than abused adolescents who reported little support from other adults. Similarly, as positive view of the future increased, the likelihood of having engaged in sexual intercourse increased for abused adolescents. The independent variables in the model explained 18% of the variance. The fifth logistic regression equation examined the problem behavior of antisocial behavior. The following variables were significant: religiosity, peer group characteristics, school climate, and involvement in extra-curricular activities. Involvement in extra-curricular activities was the only variable positively associated with antisocial behavior. The significant independent variables in the equation explained 26% of the variance in the model. Five factors were significant for the variable of suicide. Abused adolescents who reported strong family support, positive school climate, positive peer group characteristics, or a positive view of the future were less likely to have attempted suicide. However, abused adolescents who reported support from other adults were more likely to have considered or attempted suicide. Collectively, the significant variables explained 11% of the variance. Three factors were negatively associated with the problem behavior of purging, these included: family support, peer group characteristics, and involvement in extra-curricular activities. The significant variables explained 2% of the variance. For the thriving behavior of school success, positive school climate, positive peer group characteristics, and involvement in extra-curricular activities were associated with an increased likelihood of high grades in school. However, abused adolescents who reported support from other adults were more likely to do poorly in school. The significant independent variables in the equation explained 14% of the variance in the model. The following variables were positively related to the thriving behavior of helping others: religiosity, other adult support, peer group characteristics, and involvement in extra-curricular activities. The independent variables in the model explained 17% of the variance.

Discussion Based on previous resiliency research (Masten & Coatsworth, 1998), we expected to find a significant association between physical abuse and risk behaviors. This hypothesis was supported with the aggregated dataset; adolescents who reported being physically abused were more likely than their non-abused peers to engage in each of the risk behaviors. However, it is important to point out that many abused adolescents did not engage in risky behaviors. Logistic analyses were employed to address the question: What factors might serve as protective factors to reduce engagement in risk behaviors and to increase engagement in thriving behaviors among abused adolescents? We utilized protective factors that had been linked to resiliency in one outcome area in previous research. We expected that increasing levels of religiosity, positive view of the future, family support, parent-adolescent communication, other adult support, positive peer group characteristics, positive school climate, and involvement in extra-curricular activities would be linked to a reduction in engagement in risk behaviors (i.e., alcohol use, tobacco use, drug use, sexual activity, antisocial behavior, attempted suicide, and purging) among abused adolescents. Overall, the importance of the results is in the consistency of specific protective factors across various risk behavior and thriving behavior domains. Indeed, several protective factors were found to be significant

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for increasing the likelihood that physically abused adolescents would not engage in any of risk behaviors but who were more likely to engage in the thriving behaviors. This finding indicates that specific protective behaviors may increase resiliency in multiple areas. Given that Luthar and her colleagues (Luthar et al., 2000) find, “some high-risk children manifest competence in some domains but exhibit problems in other areas” (p. 548), the finding of the current study provides encouraging insight into the importance of increasing environmental protective factors to ensure that at-risk adolescents (e.g., physically abused) beat the odds. The variance accounted for in the models ranged from 26% (alcohol use and antisocial behavior) to 2% (purging behavior). Peer group characteristics was a significant predictor in all seven of the logistic regressions, followed by positive school climate (six models), religiosity (five models), other adult support (five models), family support (four models), view of the future (two models), and involvement with extra-curricular activities (two models). Parent-adolescent communication was not found to be a significant predictor in any of the risk behavior equations. Parent-adolescent communication may not have contributed uniquely to predicting engagement in risk behaviors because it is highly correlated with family support (r = .73). Adolescents who reported high levels of family support also reported open lines of communication with their parents. If family support is not included in the analysis presented in Table 3, parent-adolescent communication is associated with a decreased risk of purging (p < .001); once family support is controlled, this relation is reduced to non-significance. The results for support from other adults, view of the future, and involvement in extra-curricular activities were inconsistent with our expectations. In fact, having a close relationship with an adult outside the family was associated with an elevated risk of engaging in five risk behaviors (i.e., alcohol use, tobacco use, drug use, sexual activity, and suicide) in the logistic regression analyses when other factors were controlled. It is difficult to interpret this counterintuitive finding. One possible interpretation that we considered was that selection factors are involved; adolescents who seek out adults outside the family as confidants may do so because of a perceived inability to find the support they need from their parents. In fact, this interpretation was somewhat supported by the data, in that the correlation between abused adolescents who reported supportive relationships with adults outside the family was not highly correlated (.21) with supportive relationships with their parents. However, to provide strong support for this link, the direction of the correlation needs to be negative. The correlation was significant given the large sample size. Another possible explanation is that non-family adults the adolescent looks to for support may not be the people we envisioned when we selected this variable for the analysis. We thought of the outside adult support person as a mature person who is capable of providing the adolescent with guidance and insights based on a wealth of life experiences, when in fact it may be that the adult is the adolescent’s supervisor at a fast food restaurant who recently completed high school. Unfortunately, no additional information is available on the relationships between the adolescents and the non-familial adults they identify as confidants. In another way, the data were not consistent with our hypotheses. As expected, view of the future was associated with a decreased likelihood of engaging in suicide; however, it was positively associated with sexual activity. One possible explanation is that youth’s egocentric thinking, the perception that youth have of themselves as invincible and invulnerable, increases their likelihood of engaging in risk behaviors (Arnett, 1995). Thus, youth who believe in a very positive future may in fact be inflated by the same egocentric thinking. Finally, involvement in extracurricular activities was found to increase antisocial behavior. We are not sure how to explain this finding. Perhaps participation in more physically aggressive sport activities (e.g.,

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football, soccer, basketball) plays a role but without more information about the extracurricular activities, we are not able to provide a plausible explanation. With regard to the two logistic regression models for the thriving behaviors, the following variables were found to be significant in the two models: other adult support, peer group characteristics, and involvement in extra-curricular activities. In addition, for school success, positive school climate was also a significant predictor, while religiosity was a significant predictor for helping others. Support from other adults had a mixed influence on the thriving behaviors. It was negatively related to school success but positively related to helping behavior. Because engagement in some of the risk behaviors is a relatively uncommon behavior among adolescents, and because a minority of adolescents reported being abused, a very large sample was required to examine the relations of interest. Fortunately, this dataset provided an unusual opportunity to explore the relations of interest with a large sample. However, it is important to acknowledge the limitations of this study. Because the survey was designed to measure a broad range of topics, the amount of information available on any one variable was limited. For example, sexual activity was measured with a single item. Similarly, only a single item was used to measure physical abuse. It would have been useful to have additional information about the abuse experience, including who the perpetrator was, the age at which the abuse occurred, how recent the abuse was, and the duration of the abuse experience. The complexity of defining child abuse (e.g., physical) has been noted by several investigators (Finkelhor, 1994; Giavannoni, 1989; Mash & Wolfe, 1991). All of these factors could influence the extent to which the adolescents were symptomatic. Information about the abuse history, eating behaviors, and current family circumstances from an additional source would also have been valuable for this investigation, but it would not be practical to collect such information in large scale surveys like the ones used for this study. In addition, it is important to emphasize that no causal relations can be determined with the available data. We could only determine whether the relations among the variables were consistent with our hypotheses. A priority for future research is to increase the understanding of the factors mediate the relationship between physical abuse and engagement in risky behaviors. This may be particularly useful for identifying which victims of child abuse are at highest risk of engaging in certain risk behaviors. Such research may also prove helpful to those devising treatment plans for adolescents who engage in the health-compromising behavior. References Arnett, J. D. (1995, March). Developmental contributors to adolescent reckless behavior. Paper presented at the meeting of the Society for Research in Child Development, Indianapolis, IN. Benson, P. L. (1990). The troubled journey: A portrait of 6th–12th grade youth. Minneapolis, MN: Search Institute. Benson, P. L., & Roehlkepartain, E. C. (1993). Youth in single-parent families: Risk and resiliency. Minneapolis, MN: Search Institute. Block, J. (1971). Lives through time. Berkeley, CA: Bancroft Books. Blum, R. W., Beuhring, T., & Rinehart, P. M. (2000). Protecting teens: Beyond race, income, and family structure. Minneapolis, MN: Center for Adolescent Health, University of Minnesota. Blum, R. W., McNeely, C. A., & Rinehart, P. M. (2002). Improving the odds: The untapped power of schools to improve the health of teens. Minneapolis, MN: Center for Adolescent Health, University of Minnesota. Blyth, D. A. (1993). Healthy communities, healthy youth: How communities contribute to positive youth development. Minneapolis, MN: Search Institute.

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Résumé Objectif: Cette étude examine la relation entre les mauvais traitements physiques et certains comportements à risque ainsi qu’avec des comportements sains. On y examine également la relation entre des comportements pouvant eˆ tre facteurs de protection et l’engagement dans les comportements à risque ou sains chez les victimes de mauvais traitements physiques. Trois catégories de facteurs potentiels de protection ont été examinés: 1. les caractéristiques individuelles; 2. le fonctionnement familial; 3. les facteurs extra-familiaux. Nous nous attendions à ce que de hauts niveaux de facteurs de protection réduiraient l’engagement dans des comportements à risque (i.e., le recours à l’alcool, la consommation de tabac ou de drogue, l’activité sexuelle, les comportements anti-sociaux, une tentative de suicide ou le recours compulsif aux purges chez les adolescents qui ont été abusés). Résultats: En ce qui concerne les comportements à risque, les adolescents maltraités ont déclaré s’y eˆ tre plus souvent engagés que les adolescents qui n’avaient pas été maltraités. Plusieurs facteurs de protection ont été identifiés concernant les sept comportements à risque. Les caractéristiques du groupe des pairs ont constitué un prédicteur significatif dans toutes les sept régressions logistiques, suivies par un climat scolaire positif, la pratique religieuse, le soutien d’un autre adulte (5 modèles), le soutien de la famille (quatre modèles), une perspective pour le futur (deux modèles), et l’investissement d’activités autres que scolaires. La variance prise en compte par les modèles s’étendait de 2% (comportement à risque de recours aux purges) à 26% (comportement à risque lié à la consommation d’alcool et comportement antisocial). Conclusion: Les résultats montrent que à l’exception de l’activité sexuelle, la majorité des adolescents ne s’engageaient pas dans des comportements à risque. Toutefois un nombre significativement supérieur d’adolescents abusés s’engageaient dans des comportements à risque par rapport à ceux qui n’avaient pas été abusés. De plus, le fait que l’on a trouvé qu’il existait des facteurs de protection à différents niveaux du milieu écologique des adolescents, permet d’envisager d’importantes conséquences pour la pratique.

Resumen Spanish language abstract not available at time of publication.