Child Maltreatment and Quality of Life Among Urban Chinese Children

Abstract

Child maltreatment is an international public health problem. The aftermath can appear in multiple aspects of individual health and development, including decreased physical and emotional function, posttraumatic stress disorder (PTSD), and suicide attempts. However, there is lack of evidence regarding the quality of life of maltreated children. This study aims to examine the association between children’s maltreatment experience and their quality of life. This study employed a cross-sectional design and surveyed 1,093 primary school children (including migrant children and nonmigrant children) in Shanghai, China. The Parent–Child Conflict Tactics Scale (CTSPC) and Pediatrics Quality of Life Inventory were used to measure children’s experiences of child maltreatment and their quality of life. Corporal punishment (89.47%) was the most prevalent form of child maltreatment, followed by physical assault (80.21%), psychological abuse (65.00%), and neglect (52.26%). Children’s migration status was not associated with their quality of life; however, social capital factors were all associated with increased levels of quality of life. Children’s victimization experience, including severe to very severe physical assault (β = −3.34/−3.80, p < .001), psychological assault (β = −3.74, p < .001), corporal punishment (β = −2.46, p < .01), and neglect (β = −4.31, p < .001), was associated with their decreased quality of life. The results show that child maltreatment threatens the quality of life of children. There is a need for developing effective interventions to prevent child maltreatment and reduce the adverse outcomes among children with maltreatment experiences.

Keywords

neglect child abuse physical abuse prevention of child abuse mental health and violence prevention sexual assault

Introduction

Child maltreatment is an international public health problem (Tran et al., 2017). Although the definition of child maltreatment has been debated as related to cultural and social contexts (McElvaney & Lalor, 2014), the forms of child maltreatment are widely recognized: physical abuse, neglect, emotional abuse, and sexual abuse to children under the age of 18 (Chahine, 2014). Child maltreatment happens in every society across the world with a prevalence of 4.9% to more than 90% (Chan, 2013; Euser et al., 2015; Stoltenborgh et al., 2011; Straus et al., 1998). A maltreated child exposed to multiple forms of violence or victimization may experience even more severe consequences to their health and well-being (Finkelhor et al., 2007). Norman and colleagues (2012) argue that most studies focus on the influences of child maltreatment on psychological functioning. However, a wider range of understanding has yet to be addressed, especially regarding the overall quality of life of the child. Quality of life is a multidimensional concept, including a child’s physical, emotional, social, and day-to-day function at school (World Health Organization [WHO], 1995). To properly address a maltreated child, a wide range of services that bridge mental, social, and medical services may enhance the overall well-being.

The extent to which child maltreatment can influence child physical and psychosocial health largely depends on the forms of maltreatment, genders, and the geographical origins of the child (Weber et al., 2016). For instance, most maltreatment against child occurs at home; however, social and cultural norms in a community also play a role in how child maltreatment is accepted or tolerated. The extent of community support available to the child may also influence the effects of child maltreatment. Notwithstanding this assumption, different types of samples, different aspects of child maltreatment, and different measures of child maltreatment and well-being also influence the degree of effect size. Thus, the previous investigations of child maltreatment and well-being have missed many important links, such as the potential association between different types of child maltreatment and overall well-being within one sample as well as the confounding effect of other variables.

According to Ji and Finkelhor’s (2015) study of the children from the Chinese mainland, Hong Kong, and Taiwan, about 36.6% children suffered from physical abuse, which means there were almost 100-million child victims in China. Chan (2013) reported that 71.4% of children (15–17 years old) experienced at least one form of victimization during lifetime using a large Chinese sample. Recent social media reports of child abuse cases in China, mostly among internal migrant families, have attracted increased attention. But, for the most part, child maltreatment in Mainland China is under-investigated.

One consideration in attempts to understand child maltreatment in China has been migration. For example, internal migration, with 75% of the population initially relocating from rural areas in Mainland China, is no easier than interstate migration. About 34.26-million children aged 0 to 17 years moved from their rural hometowns to cities with their parents by 2015 (Lv et al., 2018). Recent meta-analyses have found that children from migrant families incur an increased risk of injury and health problems (Chen et al., 2019; Sun et al., 2016). Migrant children in China were more vulnerable to unintentional injuries, physical abuse, and neglect because their parents had little time or knowledge to provide care (Chen et al., 2019; Gao et al., 2017). However, limited attention has been paid to other forms of child maltreatment among migrant children in Mainland China (Chen et al., 2019). Migration brings people out of impoverished areas to more influent areas, but also causes family stress. Drawing upon the ecological model (Belsky, 1980), at the family and community levels, most rural-to-urban migrant families in China are at a lower socioeconomic status than their urban local counterparts (Sun, 2017), often have poor social relationships with urban residents, and most of them can only attend privately run migrant schools with poor built environments (Umobong, 2010). However, Cheung (2014) found migrant children in Guangzhou, China, can employ social ties as a coping strategy, and thereby mitigate some of the negative effects of migration on health. Studies have also found links between migration and family separation, parental divorce, and other crises that may influence child well-being (Larson et al., 1990; Schick et al., 2016). At the social/cultural level, the lack of a local hukou (household registration) results in a lower social status for migrant families in urban areas and an inability to access local institutions, benefits, and services available to residents. The unequal treatment toward migrant families in the public service system poses risks to the stability and sustainability of healthy child development. Indeed, some migrants who are business owners are free from economic stress. Very few migrant children can enroll in public schools given that public schools set higher fees and special entrance exams for migrant children. Generally, urban local children have greater advantages at birth in terms of their socioeconomic status, access to rich resources, and urban policy benefits. However, children in low-income, single-parent, and more fragile settings are at higher risks of maltreatment (Alink et al., 2013; Alvarez et al., 1988; Stith et al., 2009; Tan et al., 1991). The combination of conflict and lower levels of opportunities can culminate in serious physical and psychosocial consequences for migrant children (Miller & Jordans, 2016). Previous studies concluded that there is a significantly negative association between child maltreatment and child quality of life (Chan et al., 2017; Tran et al., 2017). Gao et al. (2017) and Wong et al. (2009) found that internal migration was associated with higher risks of child abuse, physical abuse, and psychological abuse. However, the relationship between child maltreatment and quality of life among Chinese migrant children remains largely unknown.

Considering the research gaps indicated by previous studies, this study addresses the following issues. First, it examines the child-reported prevalence rates of maltreatment and the differences associated with key sociodemographic factors (migration status and gender). Second, it assesses the association of a child’s quality of life with child maltreatment and other covariates. Based on the previous findings on the impacts of gender, family structure, school type, social capital, and so on on child quality of life, we included several factors at the individual, family, and community level as covariates.

Method

Study Design

We conducted a cross-sectional, school-based survey study on a sample of Chinese children in a suburban area in Shanghai in 2015. Shanghai is the second most-populous city of China with a population of over 1.33-million migrant children accounting for 14.24% of children living in this city (Duan et al., 2013). We employed a convenience sampling method, including 12 primary schools. All children in the fifth to sixth grade (11–13 years old) were eligible to participate in this study and were given a study information sheet and asked to sign a written consent form before joining the study. Informed consent was obtained from participant’s parents or main caregivers as well. Participants completed the survey with the assistance of trained interviewers, and they were reassured about their rights to terminate or withdraw from the study at any time. All procedures in this study were approved by the Institutional Review Board of the University of Hong Kong.

Measures

Child quality of life

The Pediatric Quality of Life Inventory (PedsQL) was designed as an instrument to measure the core dimensions delineated by the WHO plus the role functioning of a child (school functioning). It is a multidimensional scale comprised of 23 items (WHO, 1995), measuring physical (eight items), emotional (five items), social (five items), and school functioning (five items). Each question reflects the frequency of a child’s experience of functioning problems in the past one month (e.g., It is hard for me to walk more than a block), rated on a 5-point Likert-type scale ranging from 0 to 4 (0 = never, 1 = almost never, 2 = sometimes, 3 = often, 4 = almost always). In this study, the summary score of each participant were reverse coded. So, the higher score represents fewer functioning problems, and reflects higher a level of quality of life of the child. The total score for quality of life was calculated as the sum of the scores divided by the number of items answered, ranging from 0 to 100. The Cronbach’s alpha for the resulting scale was .87. The Physical (Cronbach’s α = 0.74) and Psychosocial (Cronbach’s α = 0.70) subscales also showed good internal consistency.

Child maltreatment

The children also filled out the Parent–Child Conflict Tactics Scale (CTSPC; Straus et al., 1998), which has shown good performance in previous studies in terms of respondent comprehension, reliability, and validity. The survey assesses the occurrence of nonviolent discipline, psychological abuse, corporal punishment, physical abuse, and neglect in the preceding 12 months. In this study, the scales showed a good reliability (Cronbach’s α = 0.91).

Child social capital

The measurement of the children’s social capital consists of two parts: the Social Capital Questionnaire for Adolescent Students (SCQ-AS; Paiva et al., 2014) and the parental autonomy support subscales of Perceptions of Parents Scales (child POPS; Grolnick et al., 1991). The scales include six items: (a) school cohesion, (b) friendship, (c) neighborhood social cohesion, (d) trust in school/neighborhood, (e) maternal autonomy support, and (f) paternal autonomy support. The Cronbach’s alphas of the six subscales are 0.44, 0.60, 0.83, 0.56, 0.53, and 0.67 respectively.

Other variables

Individual factors include the gender, age, and migration status of the child. At the family level, information about the number of siblings, living arrangement, family structure, parental education, employment, and income were also collected in the survey. At the community level, school type was also recorded.

Statistical Analyses

Participants’ profiles were summarized using descriptive statistics, including the children’s gender, age, the number of siblings, family structures, and parents’ education and employment status. The mean scores and standard deviation (SD) of the scales measuring child quality of life, along with the frequencies and prevalence rates of child maltreatment, were computerized. In addition, t tests or chi-square tests were employed to examine group differences in child maltreatment and quality of life related to migration status and gender of the children.

We conducted multiple regression analyses to explore the association of quality of life with child maltreatment and other covariates. Effects of sociodemographics and social capital were examined by using three-stage regression models. In this study, Model 1 included sociodemographic variables only; Model 2 included sociodemographics and social capital variables; and Model 3 included child maltreatment with sociodemographic and social capital factors. All tests were two-tailed and performed with STATA 15.0, and the statistical significance level was set at .05.

Results

The final sample consisted of 1,628 completed self-reports in a district of Shanghai (response rate = 81%). Details of the characteristics of the sample are presented in Table 1. The sample comprised 44.17% girls, and 55.83% boys. The mean age of all children in the sample was 11.08 years (SD = 0.81). Over 45% of the children attended public schools. About 80% of the children did not obtain a local hukou. In all, 55.9% were only children, 35.85% had one sibling, and 8.25% had two or more. Most parents of the surveyed children did not complete a high school education. A minority of the parents, between 3.33% and 3.46%, were single, widowed, separated, or divorced. About one in five children had at least one parent who was unemployed. Almost the entire sample was living with at least one of their biological parents (97%). The average annual income of the children’s families was around 6,000 to 7,000 RMB (US$850–US$1,100).

Table 1.

Descriptive Characteristics of Sample (N = 1,628).

	Frequency (%)
Variables	n
Gender (girls)
Girls	44.17
Boys	55.83
Age, M (SD)	11.08 (0.81)
Number of siblings, M
None	55.9
One	35.85
Two or more	8.25
Attends public school (ref: private)	45.27
Nonlocal hukou (Ref: local)	80.05
Father educational attainment
Below high school education	65.35
High school completion or higher	16.69
Associate’s or higher degree	3.55
Bachelor’s or higher degree	12.37
Master’s or higher degree	2.47
Mother educational attainment
Below high school education	69.68
High school completion or higher	14.06
Associate’s or higher degree	3.93
Bachelor’s or higher degree	10.26
Master’s or higher degree	2.26
Mother employed	81.36
Father employed	97.35
Father marital status
Married/cohabiting	96.54
Widowed/separated/divorced	3.46
Mother marital status
Married/cohabiting	96.67
Widowed/separated/divorced	3.33
Single-parent family	2.82
Living with parent(s)	97.01
Household annual income, M	6,000–7,999 RMB

Table 2 shows the mean scores of the quality of life or social capital of children assessed in this study. Overall, the mean scores of the PedsQL were 82.65 (SD = 0.27). The scores of six dimensions of social capital were 11.08, 8.57, 5.09, 7.89, 16.02, and 16.15, respectively. We compared the scores of PedsQL and social capital between local children and nonlocal children and found that local children reported higher scores in physical functioning; however, there was no significant difference in terms of the total quality of life or psychosocial functioning. Nonlocal children reported higher scores in neighborhood cohesion and trust in neighborhood and school compared with their local counterparts. In addition, local children reported higher scores in school cohesion, maternal support, and paternal support in comparison with their nonlocal counterparts.

Table 2.

Quality of Life and Social Capital Among Sample (N = 1,607).

	Score (M, SD)
Variables	Total	Local (320)	Nonlocal (1,287)	Difference (p value)
Quality of life	82.65, 0.27	83.5, 0.59	82.43, 0.30	.06
Physical functioning	90.52, 0.28	92.08, 0.59	90.13, 0.32	.003
Psychosocial functioning	78.44, 0.30	78.92, 0.70	78.32, 0.34	.22
Social capital
School cohesion	11.08, 0.07	11.21, 0.06	11.05, 0.03	.017
School friendship	8.57, 0.02	8.63, 0.05	8.56, 0.03	.142
Neighborhood cohesion	5.09, 0.03	4.94, 0.07	5.13, 0.04	.009
Trust	7.89, 0.08	7.61, 0.07	7.96, 0.04	.000
Maternal support	16.02, 0.08	16.28, 0.14	15.95, 0.09	.045
Paternal support	16.15, 0.08	16.47, 0.16	16.08, 0.09	.026

Table 3 presents the prevalence rates of four types of child maltreatment by hukou type (local vs. nonlocal). The majority of the children reported experiencing at least one type of violence in the preceding year. Breaking down these results into different aspects of maltreatment, the most common aspect was corporal punishment (89.47%), followed by nonviolent discipline (80.61%), very severe assault (72.57%), psychological abuse (65.00%), physical abuse (53.58%), and neglect (52.26%). Group differences between local and nonlocal children were identified in nonviolent discipline, physical abuse, and very severe physical assault.

Table 3.

Prevalence Rate of Child Maltreatment Among Sample by Hukou Type (N = 1,675).

	Prevalence Rate (%)
Variables	Past-Year Prevalence	Local	Nonlocal	Difference (p value)
Nonviolent discipline	80.61	84.11	79.11	.045
Psychological aggression	65.00	65.12	65.14	.997
Physical assault
Corporal punishment	89.47	90.97	88.43	.195
Physical abuse	53.58	50.46	34.38	.000
Very severe assault	72.57	70.3	63.57	.040
Neglect	52.26	54.78	50.04	.133

Gender differences were also observed in some parameters. Table 4 shows that boys reported higher prevalence rates in psychological aggression (boys = 69.05%, girls = 60.86%, p = .001), physical abuse (boys = 52.95%, girls = 45.49%, p = .008), and neglect (boys = 56.71%, girls = 47.12%, p = .000).

Table 4.

Prevalence Rate of Child Maltreatment Among Sample by Gender (N = 1,567).

	Prevalence Rate (%)
Variables	Past-Year Prevalence	Boys (%)	Girls (%)	Difference (p value)
Nonviolent discipline	80.61	80.18	81.94	.364
Psychological aggression	65.00	69.05	60.86	.001
Physical assault
Corporal punishment	89.47	90.42	89.1	.380
Physical abuse	53.58	52.95	45.49	.008
Very severe assault	72.57	71.69	69.63	.372
Neglect	52.26	56.71	47.12	.000

A series of multiple regression analyses were conducted to examine the associations between child maltreatment or social capital and child quality of life. The findings are summarized and presented in Table 5. We first added several sociodemographic variables in Model 1. A child who has a father working out is positively related to higher score of quality of life. In Model 2, we added the six parameters of social capital, showing that all six variables were positively related to quality of life of the children. For instance, school cohesion is positively associated with quality of life (β = 1.02, p < .001). Paternal positive support is associated with quality of life of the children (β = 0.26, p < .01). Then, we added the five aspects of child maltreatment one by one in the last model, showing that they were all negatively associated with the overall child quality of life. The strongest association appeared between child neglect and child quality of life (β = −4.31, p < .001). A child who experienced psychological aggression reported lower score in quality of life compared with a child who did not (β = 3.74, p < .001). The overall quality of life of the children who reported severe physical assault is lower than those who did not (β = −3.34, p < .001). Table 6 shows the association of physical and psychosocial functioning with five forms of child maltreatment. The association of physical functioning and neighborhood cohesion is not statistically significant at 95% confidence level. Child reported corporal punishment is not significantly associated with their physical functioning at 95% confidence level. Despite that, most results are in line with the results predicted in Table 5.

Table 5.

Three Models for Testing the Relationship of Quality of Life With Sociodemographics, Social Capital, and Child Maltreatment (N = 1,548).

	Quality of Life
Variables	β (95% CI)	p	R ²
Model 1: Sociodemographics			.01
Child gender (girl)	0.34 [–0.79, 1.47]	.557
Child age	−0.25 [–0.95, 0.45]	.483
School type (public school)	1.38 [–0.03, 2.79]	.055
Nonlocal (migrant child)	0.85 [–1.07, 2.78]	.383
Maternal education	0.22 [–0.20, 0.63]	.311
Paternal education	0.38 [–0.11, 0.87]	.125
Maternal employment (employed)	−0.04 [–1.52, 1.43]	.955
Paternal employment (employed)	5.33 [1.81, 8.85]	.003
Household income	0.05 [–0.20, 0.30]	.677
Model 2: Social capital			.15
School cohesion	1.02 [0.52, 1.52]	.000
School friendship	1.48 [0.89, 2.06]	.000
Neighborhood cohesion	0.53 [0.09, 0.98]	.019
Trust	0.90 [0.44, 1.36]	.000
Maternal support	0.50 [0.31, 0.68]	.000
Paternal support	0.26 [0.08, 0.44]	.006
Model 3
Psychological aggression	−3.74 [−4.87, −2.62]	.000	.18
Corporal punishment	−2.46 [−4.15, −0.77]	.004	.15
Severe physical assault	−3.34 [−4.52, −2.17]	.000	.19
Very severe physical assault	−3.80 [−4.95, −2.65]	.000	.19
Neglect	−4.31 [−5.36, −3.26]	.000	.19

Note. CI = confidence interval.

Table 6.

The Association of Two Aspects of Quality of Life With Different Forms of Child Maltreatment and Other Covariates.

	Physical Functioning			Psychosocial Functioning
Variables	β (95% CI)	p	R ²	β (95% CI)	p	R ²
Model 1: Sociodemographics			.03			.01
Child gender (girl)	−0.96 [−2.14, 0.21]	.108		1.01 [−0.27, 2.29]	.122
Child age	0.09 [−0.63, 0.82]	.803		−0.45 [–1.24, 0.34]	.265
School type (public school)	2.56 [1.09, 4.02]	.001		0.80 [–0.79, 2.39]	.325
Nonlocal (migrant child)	0.33 [–1.67, 2.32]	.749		1.17 [−0.99, 3.34]	.289
Maternal education	−0.02 [−0.46, 0.41]	.914		0.35 [−0.12, 0.82]	.148
Paternal education	7.38 [3.72, 11.04]	.459		0.49 [−0.06, 1.04]	.081
Maternal employment (employed)	−0.04 [−1.52, 1.43]	.248		−0.56 [−2.22, 1.09]	.504
Paternal employment (employed)	5.33 [1.81, 8.85]	.000		4.25 [0.28, 8.22]	.036
Household income	0.09 [−0.17, 0.35]	.487		0.03 [−0.25, 0.31]	.84
Model 2: Social capital			.09			.16
School cohesion	0.89 [0.52, 1.52]	.002		1.10 [0.54, 1.66]	.000
School friendship	0.83 [0.89, 2.06]	.007		1.84 [1.19, 2.50]	.000
Neighborhood cohesion	0.34 [0.09, 0.98]	.086		0.64 [0.14, 1.14]	.012
Trust	0.65 [0.44, 1.36]	.026		1.03 [0.51, 1.54]	.000
Maternal support	0.31 [0.31, 0.68]	.005		0.59 [0.38, 0.80]	.000
Paternal support	0.26 [0.08, 0.44]	.012		0.26 [0.06, 0.47]	.012
Model 3
Psychological aggression	−2.70 [−3.93, −1.47]	.000	.10	−4.32 [−5.59, −3.06]	.000	.19
Corporal punishment	−1.19 [−3.03, 0.67]	.207	.09	−3.13 [−5.02, −1.23]	.001	.17
Severe physical assault	−2.42 [−3.72, –1.12]	.000	.11	−3.85 [–5.18, −2.51]	.000	.20
Very severe physical assault	−2.96 [−4.24, −1.68]	.000	.11	−4.23 [−5.52, −2.92]	.000	.19
Neglect	−3.52 [−5.36, −3.26]	.000	.12	−4.71 [−5.90, −3.52]	.000	.19

Note. CI = confidence interval.

Discussion

Based on the cross-sectional school-based survey data collected in Shanghai, this study is one of the first to provide a profile of the associations between different types of child maltreatment and child quality of life. It also demonstrates the relationship of social capital and child quality of life. This study extends our understanding of the prevalence of child maltreatment experienced by local and migrant (nonlocal) children in a suburban area of Shanghai.

First, our data suggest that migrant children do not necessarily suffer poorer psychological health or overall quality of life than their urban counterparts. This observation is similar to the findings of previous studies on China’s migrant adolescents and adults (Cheung, 2013). The “healthy migrant hypothesis,” which postulates that healthier individuals tend to migrate (Palloni & Morenoff, 2001), may help us understand this finding.

Second, the prevalence rates of each aspect of child maltreatment are relatively high among our sample compared with the results (10%–74%) of previous studies among Chinese children (Lin, 2018; Yang et al., 2014). Previous studies have shown that the prevalence rate of child maltreatment in Asian countries where the tolerance of corporal punishment of children is more tolerated culturally is higher compared with Western society (Stoltenborgh et al., 2013). In addition to the factors at the individual, family, and community levels examined in this study, cultural values such as filial piety might influence child maltreatment (Liao et al., 2011). The power disparity between parents and children embedded in traditional Chinese culture may endow parents with power to control and punish their children. We observed that local native children reported higher rates of physical abuse and very severe physical assault compared with the nonlocal ones, which is opposite to some results in existing literature (Chen et al., 2019; Gao et al., 2017). Different sample may result in the disparity in prevalence rates. Gao et al. (2017) surveyed middle school students in Shenzhen, China, finding that migrant children reported higher prevalence rates of physical and psychological maltreatment in the preceding year compared with their local counterparts. In that study, 41.98% of migrant children’s parents and 79.44% of local children’s parents received education of high school and beyond. Low parental educational attainment of our sample may help to explain the disparate results. The children in the current study lived in suburban areas of Shanghai, and most (65.35%) of their parents did not complete high school. Therefore, the poorly educated parents may still believe that physical punishment is beneficial to children’s future because it makes them more obedient and respectful of parental authority. Although the local parents live in Shanghai, in which the traditional Chinese culture is waning due to the influence of Western and modern culture, the punishment-oriented parenting styles are still popular in suburban areas. In addition, boys are more vulnerable to maltreatment compared to girls in terms of psychological aggression, physical abuse, and neglect, which is in line with previous findings. Therefore, children of parents with low educational attainment who live in suburban areas can be our targeted group when implementing child maltreatment prevention programs.

Notwithstanding this, informal social control may play a salient role in the migrant community determined by their living environment, which can explain the relevantly lower reported rates among migrant children. As formal social control (police and child protection workers) of child maltreatment is less developed in Mainland China, neighbors are more willing to get directly involved where the corporation and personal relationships are stronger (Emery et al., 2015). Migrants from the same hometowns are very likely to reside in a community where the neighbors are familiar with each other. The results showed that migrant children reported higher scores in community cohesion and trust compared with their local counterparts, indicating a good relationship within their neighborhood. As Emery and colleagues (2015) mentioned, the prevalence rates of severe child abuse were much lower in a community where informal social control is stronger. Most migrant families lived in high-density slum houses instead of apartment buildings, so parents are less likely to employ harsh punishment that may easily attract neighbors’ attention.

Third, any form of child maltreatment is detrimental to the overall quality of life of children. Specifically, psychological aggression, (very) severe physical assault, and neglect harm not only physical but psychosocial functioning of the child. Most children in this sample reported their parents employed corporal punishment, which is a very common disciplinary approach in Chinese families. It is noteworthy that this common discipline harms the psychosocial functioning of children in the current study.

Fourth, we by no means intend to draw attention to the effect of migration status on children’s quality of life; however, we found social capital can promote the quality of life of children. All local children could attend public schools, while only some migrant children could attend public schools depending on the number of unoccupied seats left for them. Therefore, public school provides a more friendly and cohesive environment for children. In addition, local parents could give more support to their children while migrant parents might be too busy to interact with their children. Although local native children reported more adverse experiences compared with migrant children, they did not necessarily suffer from worse quality of life. It is possible that local children benefit from higher level of school cohesion, maternal positive support, and paternal positive support, which buffers their overall quality of life. Social workers should improve the schooling environment and parental support to improve the quality of life of children.

Findings from this study should be interpreted with the following caveats. First, there might be potential bias in child self-reports related to their recalling experiences of maltreatment. The recall bias could inflate or confound the association of child maltreatment and quality of life. We have tried to reduce bias by employing a well-validated measurement (CTSPC). Second, we did not include sexual abuse as a form of child maltreatment in this survey. The removal of this sensitive issue was meant to promote a higher response rate in a Chinese sample and protect children from any unexpected impact of the research. Despite that, this study focuses on examining parent-to-child maltreatment, whereas perpetrators of sexual abuse seldom are parents of children. Third, the results of the current sample are not representative due to the limited data collection ability. Prevalence rates among our sample can be higher compared with the general population because economic hardship may cause stress and increase child maltreatment. Last, the cross-sectional nature of this study made it impossible to conclude on any causative relationship. We proposed the hypotheses based on a theoretical support that child maltreatment precede functioning problems; however, the use of cross-sectional data for examining is prone to challenges on the direction of influences because all factors are measured simultaneously. Therefore, the findings of this study should be understood from a theoretical perspective.

Conclusion and Implications

The major strength of this study is that it examines the prevalence of different types of child maltreatment and their associations with child quality of life within one sample. It allows us to identify the incidence of child maltreatment among this suburban sample. To our knowledge, it is one of the first studies using one single sample to provide a complete profile of the links between different aspects of child maltreatment, social capital, some sociodemographic variables, and quality of life. It helps to identify the risk and protective factors related to child maltreatment at different ecological levels, which provides meaningful implications for preventing child maltreatment and reducing the deleterious effects on child quality of life. As a lack of understanding of its cost and consequences hinders investment in prevention policies in Mainland China, this study expands the scientific evidence base for the magnitude and consequences of child maltreatment among migrant children in Shanghai.

Footnotes

Acknowledgments

Special thanks go to Professor Ko Ling Chan, who gives valuable suggestions and comments to this manuscript.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is sponsored by Shanghai Pujiang Program (2010PJC025) and Shanghai Philosophy and Social Science Planning Project (2019ESH003). It is also supported by the Fundamental Research Funds for the Central Universities.

ORCID iD

Xiaoyue Sun

Author Biography

Xiaoyue Sun, PhD, is assistant professor at East China University of Science and Technology. Her research focuses on child protection and child welfare and emphasizes the need to promote child well-being through social support and ecological improvement. She is interested in using intervention and evaluation as a tool to initiate and support policy change and in identifying mechanisms to translate research into social work practice.

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