Domestic Violence and Child Outcomes in Low- and Middle-Income Settings: Psychological, Academic, and Quality-of-Life Impacts Among School Children in Southern Nigeria

List Of Abbreviations

WHO - World Health Organisation

UNICEF - United Nations Children’s Fund

DV - Domestic Violence

CEDV - Children’s exposure to domestic Violence

SDQ - Socio Demographic Questionnaire

WHOQOL-BREF- World Health Organisation Quality of Life Brief Version

APRS – Academic Performance Rating Scale

GHQ-12 – General Health Questionnaire 12

SPSS- Statistical Package for Social Science

Introduction

Literature Review and Statement of Problem

Domestic Violence (DV) is a human rights challenge that is universal, and is very prevalent now [1].

Domestic violence is defined as a pattern of abuse of an adult member of a family by a current or former family member [1]. According to the US Department of Justice (2011), domestic violence is referred to as a pattern of abusive behaviour in any relationship that one partner uses to gain or maintain power or control over another intimate partner [1]. It encompasses physical violence, psychological, emotional, sexual abuse, economic threat, intimidation, or control [1]. The rate of domestic violence varies from one region to another, but it is said to be higher in patrilineal and patriarchal cultures [2]. It is higher in societies where women’s status is in transition, and women have begun to assume non-traditional roles or enter the workforce [2,3].

The WHO in 2005 reported that one in three to five women experiences DV during their lifetime [2]. The United Nations Population Fund (UNFPA, 2002) reported a high rate of 60% Forty-eight population-based studies conducted around the world reported that 10-69% of women have been victims of domestic violence [4,5]. In the USA, about five million women have experienced partner-related physical and sexual assault yearly, with 1000-1600 deaths occurring at the hands of their partners [6]. In West Africa, domestic violence is also prevalent but often condoned and justified. In Senegal, the majority of domestic violence goes unreported; however, about 25% of women suffer physical violence from their partners [7]. In Zambia, 27% of married women suffer partner violence each year [7].

• On average, about 50% of Nigerian women experience domestic violence [7,8]. In the south-western region of Nigeria, about 40-60% of women have experienced domestic violence [8]. In the south-eastern parts, about 70% of women experienced domestic violence [8]. while about 60% of pregnant women reported physical assault during pregnancy [9].

• Several terms have been used by researchers to describe children in households with domestic violence [10]. Early researchers spoke of these children as either witnesses or observers of violence. This term has been replaced with exposure to violence, which is more encompassing and does not make assumptions about the specific nature of children’s experience with violence [10]. Exposure to violence can include hearing or watching the violent events, being involved directly (e.g., trying to intervene or calling the police) or experiencing the aftermath, for example, seeing bruises or observing maternal depression [10,11]. Edleson et al. pointed out that the exposure to or witnessing of domestic violence encompasses multiple experiences [11]. The study reports that to witness, see, hear or even notice the mother’s injuries and to become aware of them are also considered situations of exposure to family violence [11]. The authors also specify that not only the awareness of the mother’s victimisation characterises the phenomenon, but also the victimisation of significant others who play the role of caregiver and with whom the child has a relationship or affection [10].

According to UNICEF 2007, 275 million children are exposed to domestic violence annually [12]. The number of children exposed to domestic violence varies from country to country, ranging from 240,000 to 963,000 in the UK, 339,000 to 2.7 million in the USA, 82,000 in Australia, and 0.5 to 1.3 million in South Africa [12]. A study done by Sherr et al. on exposure to violence predicts poor educational outcomes in young children in South Africa and Malawi showed that 46.7% of the children were exposed to domestic violence [13]. Another study on childhood exposure to community and domestic violence in South African students showed that 35.6% witnessed some form of domestic violence [14].

There is a dearth of studies on the prevalence of children’s exposure to domestic violence in Nigeria. A cross-sectional study done in Cambodia, Malawi and Nigeria by Kieselbach et al showed that between 22.4% and 34.3% of the study participants witnessed domestic violence during childhood [15]. Female respondents who witnessed domestic violence had higher chances of mental illness than males in Cambodia and Malawi, but male respondents in Nigeria had higher odds of mental illness. While females had no significant association between domestic violence and mental illness [15]. A study by Asagba et al. in Ibadan on the gender differences in children’s exposure to domestic violence in Nigeria showed that about 12% of the respondents were exposed to domestic violence, and there were no significant gender differences in children’s exposure to domestic violence [16].

DV affects all aspects of children’s health and well-being from conception to adulthood. It has also been found to have both physical, psychological, and social consequences that outlast the initiating abuse [17]. Exposure to domestic violence has been linked to impaired physical growth, malnutrition, brain damage,incomplete immunisation, and increased under-five mortality [17]. Mrug et al. reported that witnessing domestic violence and being a victim of violence predicted internalisation and externalisation of problem behaviours in adolescents [18]. The Centres for Disease Control (CDC) in 2006 reported that children who experienced or witnessed violence at home showed withdrawal behaviours, had low self-esteem, and were slow to show empathy [19]. They also have problems with substance abuse and engage in risky sexual behaviours [19].

Exposure to DV has been linked to damage to the brain and impaired cognitive ability [20]. As a result of the reported feelings of insecurity, children exposed to domestic violence will feel unsafe in school and have difficulties trusting adults [20]. Dyson et al. suggested that domestic violence screening is important when evaluating school failure because of the magnitude of impact of domestic violence on the academic performance of children [21]. Children who experience domestic violence have been described as underachievers academically, with low IQ, and they often experience difficulties with schoolwork and have poor attention and concentration [22-24]. Truancy, poor academic performance, & increased school dropout are some of the school-related problems reported among adolescents exposed to domestic violence [25]. Exposure to domestic violence has been associated with difficulties in mathematics, poor quality of school work, grade retention and school dropout [26]. A study by Yusuf et al. suggested that domestic violence is linked to children’s loss of interest in school, lateness to school and absenteeism [27].

WHO defines quality of life as the individual’s perception of their position in life in the context of the culture and the value systems in which they live, and about their goals, expectations, standards and concerns? [28]. In children, this would include their experiences over several domains of life. For example, physical & emotional well-being, self-esteem, & the child's relation to family, friends, & school [28,29]. Quality of life can be conceptualised in several ways, but it often includes individually appraised social, physical, emotional, behavioural and mental components. And it can be seen as an indicator of subjective health [29]. Several dimensions have been identified in children’s descriptions of their quality of life, including security and safety, freedom, fun and physical health [29]. Health complaints, both psychological and physical, are indicators of overall subjective health and adverse childhood experiences, such as domestic violence, have been associated with health complaints [29]. Since domestic violence affects children’s physical and emotional well-being as well as social and academic performance, including self-esteem, it is expected that children exposed to domestic violence will have their quality of life impacted negatively [29]. Quality of life measures would give relevant information that would go beyond the diagnosis of disorders & would also give an idea of the life domains that could be targeted for intervention improvement and functioning [29].

Here is the problem, domestic violence is quite prevalent and has an enormous and lasting impact on the affected children. The impact of domestic violence on children extends beyond the children & their families. As they have impaired learning skills, poor school performance, poor life development skills and reduced ability to self-regulate, when they grow older, society may be at risk of violence as well as increased responsibility in supporting underproductive or unproductive members of the society. Early recognition of these distressed children will lead to early institution of appropriate intervention and support, including child removal from the abusive home. This will help prevent the Intergenerational cycle of abuse and violence. Moreover, while prior research has examined psychological and academic impacts, few studies in Nigeria have concurrently assessed the effect on multidimensional quality of life using validated instrument like the WHOQOL-BREF. There are also a few studies on the number of children who witnessed domestic violence in Nigeria.

Study Hypothesis

There is an association between domestic violence and the quality of life of adolescent children.

Study Aim

To determine the impact of domestic violence on the psychological well-being, academic performance & quality of life of children in Calabar metropolis.

Study Objectives

i. To determine the proportion of children exposed to domestic violence

ii. To determine the association between exposure to domestic violence and psychological distress in children.

iii. To determine the association between exposure to domestic violence and the academic performance of children.

iv. To determine the relationship between exposure to domestic violence & the quality of life of children.

Methods

Inclusion Criteria

i. Children between the ages of 12 and 18 years (JSS 2 TO SSS3)

ii. Children whose parents gave their consent, and the child accepted to be part of the study.

Exclusion Criteria

i. Children whose parent(s) did not give their consent.

ii. Children who did not agree to be part of the study.

Procedure

The research team enlisted the services of research assistants, who were trained on how to administer the questionnaire to the subjects. The researchers visited the selected schools at a suitable break period agreed upon by the principals of the selected schools. The students were gathered in a hall, and the nature of the study was explained to them. Those who met the inclusion criteria were given consent forms to give to their parents or guardians, and assent was obtained from them. The research team returned after a week to the various schools to administer the questionnaires, which included the Socio-Demographic Questionnaire, the Child Exposure to Domestic Violence Scale, the World Health Organisation Quality of Life BREF instrument, and the General Health Questionnaire version 12, which were interviewer-administered, while the Academic Performance Rating Scale was filled by the various class teachers. The procedure was repeated until the required number of students from each level and schools were obtained. The questionnaires were identified by anonymous codes and students were made to fill the questionnaires separately. This is to ensure privacy and reduce bias During the study, children who had or are still experiencing violence (witnessing or victimisation) were referred or reported to the appropriate authorities, and appropriate interventions were instituted. Also, children with psychological distress were referred for further assessment, with intervention instituted to help them.

Sample Size

Using the formula for calculating sample size using a known proportion

(Z)²P(1-P)

    (e)²

For maximum sample size when the proportion is not known, we use

P = 50%

Z = 1.96 from a z-table at a 95% confidence interval

P = 0.5

q = (1-0.5) = 0.5

e = 5% = 0.05

(1.96)² × 0.5 × 0.5 = 384

        (0.05)²

Round off to 400.

Sampling Technique

A stratified random sampling technique was used. Information obtained from the Cross-River State Ministry of Education showed that of the 122 secondary schools, 26 are owned by the government, while the remaining 96 are privately owned. Of these, seven are single-gender secondary schools, of which only one is public and is females-only; six are private single-gender schools, four of which are females-only, while the remaining two are all-male schools. Sampling from single-gender schools will make the gender distribution uneven; therefore, single-gender schools were excluded from the sampling frame.

The multi-stage random sampling was used to select 7 private and 7 public schools for the study.

Pre-Testing

A pre-test was conducted with 10% of the sample size (40 participants) at a separate secondary school from the main study. The study was conducted by the research team with proper training from supervising consultants. The questionnaires were test-run to ensure consistency with the results obtained. The instruments were easy to administer and the time taken by the students to complete the questionnaires was noted.

Instruments

The following instruments were used to gather data from the subjects:

Sociodemographic Questionnaire

A Sociodemographic questionnaire consists of the name of the school, class, age, sex, tribe, religion, family type, marital status of parents, who the respondent lives with, father’s highest level of education, mother's highest level of education, father and mother’s occupation, monthly income of the parents and history of psychoactive substance use in the parents.

Child Exposure to Domestic Violence Scale (CEDV)

This consists of a 42-item questionnaire in three sections [30]. The first section includes a series of questions that specifically target the types of exposure to domestic violence a child might have experienced. Each child is asked to rate ten different items focused on types of adult domestic violence to which he or she may have been exposed. Each question is answered using a three-point Likert-type scale with their choices being ‘never’, sometimes’, and ‘A lot’ [11,31]. The second section of the CEDV asks 23 series of questions using the same three-point Likert-type scale. Each child is asked to rate how often he or she intervened in violent events and about other risk factors present in his or her life [30]. The third and final section of the CEDV consists of nine questions asked to gather demographic information, including gender, age, race, ethnicity, current living conditions, family composition, and concluded with a question about favourite hobbies [32]. The Cronbach alpha coefficient is 0.86 [33]. In the scoring of the scale, the value assigned to each child’s responses on the subscales and the overall total score may indicate the level of severity of the child’s experience [30]. This instrument has been used in Nigerian studies [16,34].

The World Health Organisation Quality of Life-Brief Version (Whoqol-Bref)

The WHOQOL-BREF is a widely used, cross-culturally validated tool developed by the WHO to measure quality of life in both healthy and sick people [35]. This abbreviated version includes 26 items, derived from the more comprehensive WHOQOL-100 item questionnaire [35]. It assesses quality of life across four key areas, which include physical health, psychological health, social relationship and environment [35]. The WHOQOL-BREF is widely validated and popularly used in assessing perceived quality of life of adolescents and the general population [36]. On scoring, the patient’s answers are first assigned predefined scores. These range from 1 to 5 points for all questions. For almost all questions, the worst possible health status corresponds to 1 point, while the best possible answer corresponds to 5 points. Exceptions to this are questions 3,4, and26, where you have to invert the point values;6-x (x=any score Q3, Q4 or Q23). Then, to calculate a domain score for each of the health domains by calculating the average points for each domain (sum of answer points divided by number of questions) and multiplying the result by the number 4 [37].

Physical health=4x(6-Q3) +(6-Q4) +Q10+Q15+Q16+Q17+Q18)/7

Psychological health=4x(Q5+Q6+Q7+Q11+Q19+(6-Q26)/6

Social relations= 4x(Q20+Q21+Q22)/3

Environment = 4x(Q8+Q9+Q12+Q13+Q14+Q23+Q24+Q25)/8.

The 4 domain scores are each converted into a scale from 0 to 1. For this purpose, the number 4 is subtracted from each domain's scores, and the difference is multiplied by 100/16 or the number 6.25 [37]. The 0 points represent the worst possible state of health, while 100 points represent the best possible state of health in the respective domain [37]. The WHOQOL-BREF Have been used in studies among adolescents in Nigeria [36,38]. The median split was used to categorize the domain scores into good or poor quality of life with 50 points being the cut off score. Respondents with points below 50 was categorised as having poor quality of life in each domain while those with 50 points and above was categorised as having good quality of life in each domain.

Academic Performance Rating Scale (APRS)

The APRS is a 19-item scale that was developed to reflect teachers' perceptions of children’s academic performance and abilities in classroom settings [39]. APRS includes items directed towards work performance in various subject areas, academic success, behavioural control in academic situations and attention to assignments (e.g., “how often is the child able to pay attention without you prompting him/her?”) [40]. Teachers answered each item using a 1 (never or poor) to 5 (very often or excellent) Likert scale format. Seven APRS items (nos. 12, 13, 15-19) were reverse-keyed in scoring so that a higher total score corresponded with a positive academic status [39,41]. The APRS has a Cronbach alpha value of 0.95 [41]. Interrater reliability for these measures was consistently above 95% with a mean reliability of 99% [39,41]. The instrument has been used in a Nigerian study [40].The percentile range was used to categorise the data set into poor ,average and good academic performance. Poor academic performance is below the 25th percentile (bottom 25%), average academic performance is between the 25th and 75th percentile (middle 50%). While good academic performance is above75th percentile (top 25%).

General Health Questionnaire (GHQ)-12

The general health questionnaire aims to provide information about an individual’s mental well-being by identifying distressing symptoms [42]. Its shorter version (GHQ-12item) has become one of the most widely used scales for assessing psychological distress and short-term changes in mental health, and its popularity can be mainly attributable to its brevity and easy administration [43]. It has shown a strong psychometric property, and it’s recommended as a screening tool to detect common mental disorders such as depression, anxiety and somatic disorders [43,44]. The typical scoring of the GHQ-12 uses the binary scale (0-0-1-1) and the Likert-type scale (0-1-2-). Responses to all items are summed to a total score ranging from 0 to 12 (binary scale) or 0 to 36 (Likert scale), with higher scores indicating more severe impairment. A score above a specific cutoff (3/4 for bimodal and 13/14 for the Likert scale) indicates psychological distress and suggests further investigation for potential mental disorders [44]. It has been used in several studies in Nigeria among adolescents [45,46].

Study Design

The study was a cross-sectional descriptive study. This study included School children from ages 12-18 years living in the Calabar metropolis.

This study was conducted in the Calabar metropolis which is in southern Nigeria. It comprises of 2 local government areas: Calabar South local government area & Calabar municipality [47]. According to the National population census (2006), the population of individuals living in the Calabar metropolis was put at 371,022 [47]. Using a growth rate of 2.54, the estimated population is 529,362 [47]. According to population stats.com estimated population for 2020 was put at 3 million [48].

Calabar is home to two universities, the University of Calabar and the state-owned Cross River University of Technology; three schools of paramedical sciences; and several secondary schools, including the Hope Waddell Training Institute, which was founded in 1895, as well as primary schools.

According to the Cross-River State Ministry of Education, there are 122 registered secondary schools spread across the two Local Government Areas in Calabar.

This study included public and private secondary schools located in Calabar Metropolis registered with the Cross-River State Ministry of Education.

Data Analysis

Data gotten from the study was analysed using the Statistical Package for Social Sciences (SPSS)version 25, and the p-value was set at less than 0.05.

Instruments

The following instruments were used to gather data from the subjects:

Sociodemographic Questionnaire

A Sociodemographic questionnaire consists of the name of the school, class, age, sex, tribe, religion, family type, marital status of parents, who the respondent lives with, father’s highest level of education, mother's highest level of education, father and mother’s occupation, monthly income of the parents and history of psychoactive substance use in the parents.

Child Exposure to Domestic Violence Scale (CEDV)

This consists of a 42-item questionnaire in three sections [30]. The first section includes a series of questions that specifically target the types of exposure to domestic violence a child might have experienced. Each child is asked to rate ten different items focused on types of adult domestic violence to which he or she may have been exposed. Each question is answered using a three-point Likert-type scale with their choices being ‘never’, sometimes’, and ‘A lot’ [11,31]. The second section of the CEDV asks 23 series of questions using the same three-point Likert-type scale. Each child is asked to rate how often he or she intervened in violent events and about other risk factors present in his or her life [30]. The third and final section of the CEDV consists of nine questions asked to gather demographic information, including gender, age, race, ethnicity, current living conditions, family composition, and concluded with a question about favourite hobbies [32]. The Cronbach alpha coefficient is 0.86 [33]. In the scoring of the scale, the value assigned to each child’s responses on the subscales and the overall total score may indicate the level of severity of the child’s experience [30]. This instrument has been used in Nigerian studies [16,34].

The World Health Organisation Quality of Life-Brief Version (Whoqol-Bref)

The WHOQOL-BREF is a widely used, cross-culturally validated tool developed by the WHO to measure quality of life in both healthy and sick people [35]. This abbreviated version includes 26 items, derived from the more comprehensive WHOQOL-100 item questionnaire [35]. It assesses quality of life across four key areas, which include physical health, psychological health, social relationship and environment [35]. The WHOQOL-BREF is widely validated and popularly used in assessing perceived quality of life of adolescents and the general population [36]. On scoring, the patient’s answers are first assigned predefined scores. These range from 1 to 5 points for all questions. For almost all questions, the worst possible health status corresponds to 1 point, while the best possible answer corresponds to 5 points. Exceptions to this are questions 3,4, and26, where you have to invert the point values;6-x (x=any score Q3, Q4 or Q23). Then, to calculate a domain score for each of the health domains by calculating the average points for each domain (sum of answer points divided by number of questions) and multiplying the result by the number 4 [37].

Physical health=4x(6-Q3) +(6-Q4) +Q10+Q15+Q16+Q17+Q18)/7

Psychological health=4x(Q5+Q6+Q7+Q11+Q19+(6-Q26)/6

Social relations= 4x(Q20+Q21+Q22)/3

Environment = 4x(Q8+Q9+Q12+Q13+Q14+Q23+Q24+Q25)/8.

The 4 domain scores are each converted into a scale from 0 to 1. For this purpose, the number 4 is subtracted from each domain's scores, and the difference is multiplied by 100/16 or the number 6.25 [37]. The 0 points represent the worst possible state of health, while 100 points represent the best possible state of health in the respective domain [37]. The WHOQOL-BREF Have been used in studies among adolescents in Nigeria [36,38]. The median split was used to categorize the domain scores into good or poor quality of life with 50 points being the cut off score. Respondents with points below 50 was categorised as having poor quality of life in each domain while those with 50 points and above was categorised as having good quality of life in each domain.

Academic Performance Rating Scale (APRS)

The APRS is a 19-item scale that was developed to reflect teachers' perceptions of children’s academic performance and abilities in classroom settings [39]. APRS includes items directed towards work performance in various subject areas, academic success, behavioural control in academic situations and attention to assignments (e.g., “how often is the child able to pay attention without you prompting him/her?”) [40]. Teachers answered each item using a 1 (never or poor) to 5 (very often or excellent) Likert scale format. Seven APRS items (nos. 12, 13, 15-19) were reverse-keyed in scoring so that a higher total score corresponded with a positive academic status [39,41]. The APRS has a Cronbach alpha value of 0.95 [41]. Interrater reliability for these measures was consistently above 95% with a mean reliability of 99% [39,41]. The instrument has been used in a Nigerian study [40].The percentile range was used to categorise the data set into poor ,average and good academic performance. Poor academic performance is below the 25th percentile (bottom 25%), average academic performance is between the 25th and 75th percentile (middle 50%). While good academic performance is above75th percentile (top 25%).

General Health Questionnaire (GHQ)-12

The general health questionnaire aims to provide information about an individual’s mental well-being by identifying distressing symptoms [42]. Its shorter version (GHQ-12item) has become one of the most widely used scales for assessing psychological distress and short-term changes in mental health, and its popularity can be mainly attributable to its brevity and easy administration [43]. It has shown a strong psychometric property, and it’s recommended as a screening tool to detect common mental disorders such as depression, anxiety and somatic disorders [43,44]. The typical scoring of the GHQ-12 uses the binary scale (0-0-1-1) and the Likert-type scale (0-1-2-). Responses to all items are summed to a total score ranging from 0 to 12 (binary scale) or 0 to 36 (Likert scale), with higher scores indicating more severe impairment. A score above a specific cutoff (3/4 for bimodal and 13/14 for the Likert scale) indicates psychological distress and suggests further investigation for potential mental disorders [44]. It has been used in several studies in Nigeria among adolescents [45,46].

Study Design

The study was a cross-sectional descriptive study. This study included School children from ages 12-18 years living in the Calabar metropolis.

This study was conducted in the Calabar metropolis which is in southern Nigeria. It comprises of 2 local government areas: Calabar South local government area & Calabar municipality [47]. According to the National population census (2006), the population of individuals living in the Calabar metropolis was put at 371,022 [47]. Using a growth rate of 2.54, the estimated population is 529,362 [47]. According to population stats.com estimated population for 2020 was put at 3 million [48].

Calabar is home to two universities, the University of Calabar and the state-owned Cross River University of Technology; three schools of paramedical sciences; and several secondary schools, including the Hope Waddell Training Institute, which was founded in 1895, as well as primary schools.

According to the Cross-River State Ministry of Education, there are 122 registered secondary schools spread across the two Local Government Areas in Calabar.

This study included public and private secondary schools located in Calabar Metropolis registered with the Cross-River State Ministry of Education.

Data Analysis

Data gotten from the study was analysed using the Statistical Package for Social Sciences (SPSS)version 25, and the p-value was set at less than 0.05.

Results

The findings of the study will be presented according to the study objectives.

Gender
Male	181	45.3
Female	219	54.7
Religion
Christian	388	97.0
Non-Christian	12	3.0
Family type
Monogamous	341	85.3
Polygamous	59	14.7
Parents Marital Status
Single	41	10.3
Married	325	81.3
Divorced/Separated	34	8.4
Living with
Both parents	239	59.8
Father	25	6.3
Mother	79	19.7
Others	57	14.2
Father’s educational Level
Primary	65	16.3
Secondary	145	36.3
Tertiary	190	47.4
Father’s Occupation
Civil servant	134	33.5
Self employed	266	66.5
Mother’s Educational Level
Primary	66	16.5
Secondary	155	38.8
Tertiary	179	44.7
Mother’s Occupation
Civil servant	96	24.0
Self employed	304	76.0
Witness of Domestic Violence in the past
Yes	122	30.5
No	278	69.5
Parent’s substance use history
Yes	41	10.2
No	359	89.8

Table 1: Socio-Demographic Variables

Socio-Demographic Variables of the Respondents

Table 1 showed that the mean age of the respondents was 14.8±2, with children aged 15 years and above constituting the majority. Females constituted more than half of the study population, with most respondents predominantly Christian. Respondents who have witnessed domestic violence in the past were about one-third of the study population, with a small fraction of the parents using psychoactive substances.

Variable	Yes	No	X2	df	p-value
Age(years)
Below 15	22(13.8)	138(86.2)	24.51	1	0.00*
15 and above	87(36.2)	153(63.7)
Gender
Male	51 (28.5)	130 (71.8)	0.14	1	0.70
Female	58 (26.5)	161 (73.5)
Religion
Christian	104 (26.8)	284 (73.2)	1.29	1	0.25
Non-christian	5 (41.7)	7 (58.3)
Family type
Monogamous	85 (24.9)	256 (75.1)	6.29	1	0.01*
Polygamous	24 (40.7)	35 (59.3)
Parent’s marital status
Single	12 (29.3)	29 (70.7)	12.82	2	0.00*
Married	79 (24.3)	246 (75.7)
Divorced/separated	18 (52.9)	16 (47.1)
Living with
Both parents	55 (23.0)	184 (77.0)	5.66	3	0.12
Father	9 (36.0)	16 (64.0)
Mother	25 (31.6)	54 (64.9)
Others	20 (35.1)	37 (64.9)
Father’s educational level
Primary	18 (27.7)	47 (72.3)	1.13	2	0.51
Secondary	44 (30.3)	101(69.7)
Tertiary	47 (24.7)	143 (75.3)
Father’s occupation
Civil servant	39 (29.1)	95 (70.9)	0.35	1	0.55
Self employed	70 (26.3)	196 (73.7)
Mother’s educational level
Primary	24 (36.4)	42 (63.6)	3.32	2	0.19
Secondary	39 (25.2)	116 (74.8)
Tertiary	46 (25.7)	133 (74.3)
Mother’s occupation
Civil servant	22 (22.9)	74 (77.1)	1.19	1	0.27
Self employed	87 (28.6)	217 (71.40
Witness of domestic violence
Yes	44 (36.1)	78 (63.9)	6.88	1	0.00*
No	65 (23.4)	213 (76.6)
Parental substance use history
Yes	17 (41.5)	24 (58.5)	4.65	1	0.03*
No	92 (25.6)	267 (74.4)
*Statistically significant

              Table 2: Relationship Between Sociodemographic Variables and Children’s Exposure to Domestic Violence (CEDV)

The Relationship Between Sociodemographic Variables and CEDV

Table 2 showed that there were statistically significant relationships between children’s exposure to domestic violence with age, family type, parental marital status, the witness of domestic violence and parental use of psychoactive substances.

CEDV	Frequency (n)	Percentage (%)
Yes	109	27.3
No	291	72.7

Table 3: Prevalence Rate of Children’s Exposure to Domestic Violence

The Prevalence of Children’s Exposure to Domestic Violence

Table 3 showed that the prevalence rate of children’s exposure to domestic violence was 27.3%,

CEDV
Psychological distress	Yes	No	X2	df	p-value
Present	58 (58.6)	41 (41.4)	65.16	1	0.00*
Absent	51 (16.9)	250 (83.1)
*Statistically signifacant

Table 4: Relationship Between Exposure to Domestic Violence and Psychological Distress In Children

The Relationship Between Children’s Exposure to Domestic Violence and Psychological Distress

Table 4 showed that greater number of the study participants who were exposed to domestic violence had psychological distress, and the relationship between children’s exposure to domestic violence and psychological distress was statistically significant(p=0.00).

CEDV
Academic performance	Yes	No	X2	df	p-value
Poor	72 (54.5)	60 (45.5)	74.98	2	0.00*
Average	31 (15.7)	166 (84.3)
Good	6 (8.6)	64 (91.4)
*Statistically significant

Table 5: Relationship Between Exposure to Domestic Violence and Academic Performance of Children CEDV

Relationship Between Children’s Exposure to Domestic Violence and Academic Performance

Table 5 showed that more respondents who were exposed to domestic violence had poor academic performance and the relationship between children’s exposure to domestic violence and academic performance was statistically significant (p=0.00)

Good	76 (22.0)	270 (78.0)	36.10	1	0.00*
Poor	33 (61.1)	21 (38.9)
Environment
Good	69 (22.1)	243 (77.9)	18.86	1	0.00*
Poor	40 (45.5)	48 (54.5)
*Statistically significant

                                         Table 6: Relationship Between Exposure to Domestic Violence and Quality of Life of Children

The Relationship Between Children’s Exposure to Domestic Violence and Quality of Life

Table 6 showed that respondents exposed to domestic violence had a higher prevalence rate of poor physical, psychological, social relations, and environmental qualities of life. The relationship between children’s exposure to domestic violence and the various domains of quality of life was statistically significant (p=0.00 in each domain).

Good
Social Relations
Poor	1.08	0.43	6.35	1	0.01*	2.96	1.27	6.89
Good
Environment
Poor	-0.03	0.38	0.0	1	0.92	0.96	0.45	2.03
Good
*Statistically significant. “Ref; Age<15. Ref: Male, Ref: Monogamous family, Ref: Single. Ref: witness of DV-no Ref; Parental substance use-yes. Ref: Psychological distress-present, Ref; Academic performance-poor, Ref: Physical QOl-good, Ref: Psychological QOL-good, Ref: Social relations-good, Ref: Environment-good.

                             Table 7: Binary Logistic Regression Showing the Predictors of Exposure to Domestic Violence by Children

Binary Logistic Regression Showing the Predictors and Protective Factors of Exposure to Domestic Violence by Children

Table 7 showed that respondents aged 15years and above, from a polygamous family, whose parents were married or divorced, children with prior witness of domestic violence and those with poor academic performance were more likely to be exposed to domestic violence.

Discussion

The study was a cross-sectional study carried out on secondary school pupils in Calabar metropolis to determine the effect of exposure to domestic violence on the psychological well-being, academic performance, and the quality of life of children. The mean age of the participants was 14.8 ± 2.0. The proportion of male respondents was 45.3%, while that of the females was 54.7%. The prevalence rate of children’s exposure to domestic violence was 27.3%. There was a statistically significant relationship between children’s exposure to domestic violence and psychological distress (p=0.00), academic performance (p=0.00) and quality of life (p=0.00). The study showed that the predictors of exposure to domestic violence in children include age 15years and above, polygamous family setting, married/divorced parents and poor social relations. Protective factors identified include respondents with an average academic performance, those who have not witnessed domestic violence in the past and those without psychological distress.

The results of the study will be discussed in accordance with the study objectives

Prevalence and Predictors of Children’s Exposure to Domestic Violence

This is similar to a study by Kieselbach et al on the prevalence of childhood exposure to intimate partner violence and association with mental distress in Cambodia, Malawi, and Nigeria, which showed that between 22.4% and 34.3% witnessed domestic violence during childhood [49]. Studies done by Sherr et al. and Kubeka et al. had a higher prevalence rate of 46.7% and 35.6% respectively [13,14]. These higher prevalence rates of children’s exposure to domestic violence might be due to the variability of the instruments used in assessing children’s exposure to domestic violence. The study by Sherr et al used the child report scale items from the UNICEF survey tool for orphans and vulnerable children to measure exposure to domestic violence. This tool only measured exposure to domestic violence, with only two items: if they ever saw their caregiver hit each other or if they ever saw their caregiver shout at each other. These two items may not be adequate to determine a child’s exposure to domestic violence, resulting in the high prevalence rate found in the study.

A study by Nouer et al showed that an increase in respondents' age was significantly associated with the odds of a child being exposed to domestic violence [50]. This is consistent with our regression model (AOR=4.28), which showed older adolescents (15+) were at significantly higher risk of being exposed to domestic violence. Also, studies done by Omolabake et al. and Karanja et al. showed that poor academic performance is associated with a child’s exposure to domestic violence [51,52]. This can be compared with our regression model (AOR=0.11) which showed that those with an average academic performance had lower odds of being exposed to domestic violence than those with poor academic performance . Studies have also shown that psychological distress is associated with a child's exposure to domestic violence [15]. This is similar to our regression model (AOR=0.19) which showed that the absence of psychological distress could be a protective factor for children being exposed to domestic violence. Studies relating to other predictors of children’s exposure to domestic violence, such as children from a polygamous family setting, married/divorced parents, previous witness of domestic violence, and poor social relations, were not available to the best of my search. This might be due to the paucity of studies in this subject area.

The Relationship Between Children’s Exposure to Domestic Violence and Psychological Distress

The findings is in keeping with a study by Kieselbach et al., which stated that children who witnessed intimate partner violence had higher odds of mental distress than those who did not witness it [15].

Studies by Meltzer et al and Edleson et al showed a strong association between children who experienced domestic violence with conduct disorder, anxiety and depression [31,53]. There are various pathways through which witnessing domestic violence can lead to psychological distress. Exposure to domestic violence can have direct effects, such as trauma and distress resulting from being exposed to violent situations [15]. It can also have indirect effects, such as disruption in care due to mental or physical impairment experienced by a caregiver [15].

The Relationship Between Children’s Exposure to Domestic Violence and Academic Performance

This is similar to studies done by Omolabake et al. and Karanja et al. which showed that significant relationship exists between domestic violence and poor academic performance of secondary school students [51,52]. Being exposed to domestic violence can lead to poor academic performance through various means. The academic performance of a child cannot be separated from home environment in which the child grows up [54]. Domestic violence may affect children’s participation in school as they may go to school when they are too scared to learn and a good number of them may lag behind in class as well as in life [55]. The short and long term emotional and physical aftermath of DV may affect pupil ‘s school attendance, academic performance and behavioural patterns both in school and class participation. This is because domestic violence in the context to the child’s performance affects his/her reaction to life situations and level of performance [55]. Some researchers have also found that children whose mothers were abused by their partners have intelligence quotients (IQs) lower than usual [10].

Relationship Between Children’s Exposure to Domestic Violence and Quality of Life

Studies by Grip et al.and Olofsson et al. showed that higher physical and psychological health complaints were shown by children exposed to intimate partner violence [29,56]. Children who witness DV may be less pleased to participate in social activities leading to social withdrawal, depression and post-traumatic stress. All these leads to poor psychological and physical health.

Novel Contribution

The study showed that 61.1% and 45.5% of the respondents exposed to domestic violence had poor social relations and environmental quality of life respectively. The relationship between social relations and the environment with children’s exposure to domestic violence was statistically significant. This study extends the literature by documenting the significant impact on social relations and environmental quality of life, areas previously understudied in this context.

Poor social relations, including isolation or insecure attachment, severely hinder a child’s brain development, emotional well-being and ability to form future bonds [54]. This breakdown disrupts their social network resulting in anxiety and psychological trauma. Insecure attachment leads to failure to form secure bonds with caregivers creating a foundation of fear, mistrust and poor relationship building [54]. All these severely affects a child social network.

Study Limitations

• Causality - The cross-sectional design prevents causal inference, Domestic violence exposure might lead to poor outcomes, but children with pre-existing difficulties might also exacerbate family stress.

• Social Desirability/Recall Bias – The self-administered questionnaires might have led to respondents giving favourable answers to conceal domestic abuse in the family. Also, some respondents may find it difficult to recall some traumatic experiences of the past.

• 3.Instrument - The use of GHQ-12 for assessment of psychological distress may not be very adequate, as it’s a screening tool for mental distress and may not capture the individual mental disorders resulting from children’s exposure to domestic violence.

• 4.Generalizability-. The findings are limited to school-attending children in an urban metropolis and may not reflect out-of-school children or rural populations.

Conclusion

Children exposed to domestic violence face multi-systemic harm that affects their psychological, academic and quality of life. The trauma leads to anxiety, depression and low self-esteem with resultant poor academic performance and reduced life quality. Long term consequences may include perpetuating cycles of violence and mistrust in adulthood. Intervention is crucial to breaking this cycle with early support through therapy, safe environments and community programs which can help children heal and develop resilience.

Recommendations

• For schools: “Implement routine, confidential screening for DV exposure and psychological distress using tools like GHQ-12 as part of school health programs. Train teachers and counsellors to identify signs and provide first -line support.”

• For Healthcare: “Integrate questions about DV exposure and child well-being into paediatric and maternal health consultations.”

• For Policy: “Develop and fund community- based programs that offer family counselling, conflict resolution, and safe spaces for children. Strengthen the enforcement of existing laws against domestic violence.”

• For Research;” conduct longitudinal studies to establish causal pathways and evaluate the effectiveness of school-based resilience programs for children exposed to DV.”

Declarations

Ethics approval and consent to participate

Ethical clearance was obtained from the ethics and research committee, and the research was conducted in accordance with the Helsinki declaration. Patients were informed that their participation was voluntary and that there would be no foreseeable risk. Informed written consent was obtained from the parents, and a written assent was obtained from the respondents. Referral for treatment was offered to those who screened positive for any domains assessed in the study.

Clinical trial number not applicable.

Availability of data and materials

The data set used and/or analysed during the current study is available from the corresponding Author on reasonable request.

Competing Interest

The Authors declare they have no competing interests.

Funding

There is no funding for this research.

Authors’ contributions

Ist- Conceptualised the research, supervised, and was involved in the analysis of the results.

2nd- Conceptualised the research, supervised, and was involved in the analysis of the results.

3rd- Conceptualised the research, supervised, and was involved in the analysis of the results.

4th- Involved in writing of literature, data collection, analysis and review

5th- Review of research work

All authors read and approved the final manuscript

References

1. Powell, P., & Smith, M. (2011). Domestic violence: An overview. Nevada: University of Nevada Cooperative Extension.
2. Garcia-Moreno, C., Jansen, H. A., Ellsberg, M., Heise, L., & Watts, C. H. (2006). Prevalence of intimate partner violence: findings from the WHO multi-country study on women's health and domestic violence. The lancet, 368(9543), 1260-1269.
3. Ellsberg, M., Jansen, H. A., Heise, L., Watts, C. H., & Garcia-Moreno, C. (2008). Intimate partner violence and women's physical and mental health in the WHO multi-country study on women's health and domestic violence: an observational study. The lancet, 371(9619), 1165-1172.
4. Canton, H. (2021). United Nations Population Fund—UNFPA. In The Europa Directory of International Organizations 2021 (pp. 283-285). Routledge.
5. Krug, E. G., Mercy, J. A., Dahlberg, L. L., & Zwi, A.B. (2002). The world report on violence and health. The lancet, 360(9339), 1083-1088.
6. Tjaden, P. G., & Thoennes, N. (2000). Extent, nature, and consequences of intimate partner violence. US Department of Justice, Office of Justice Programs, National Institute of Justice.
7. Abayomi, A. A. (2014). Sociological implications of domestic violence on children’s development in Nigeria. Journal of African Studies and Development, 6(1), 8-13.
8. Obi, S. N., & Ozumba, B. C. (2007). Factors associated with domestic violence in south-east Nigeria. Journal of obstetrics and gynaecology, 27(1), 75-78.
9. Okemgbo, C. N., Omideyi, A. K., & Odimegwu, C. O. (2002). Prevalence, patterns and correlates of domestic violence in selected Igbo communities of Imo State, Nigeria. African journal of reproductive health, 101-114.
10. Holt, S., Buckley, H., & Whelan, S. (2008). The impact of exposure to domestic violence on children and young people: A review of the literature. Child abuse & neglect, 32(8), 797-810.
11. Edleson, J. L. (1999). Children's witnessing of adult domestic violence. Journal of interpersonal Violence, 14(8), 839-870.
12. Dandurand, Y. (2017). Preventing violence against children: The UN model strategies. In Crime Prevention (pp. 89-120). Routledge.
13. Sherr, L., Hensels, I. S., Skeen, S., Tomlinson, M., Roberts, K. J., & Macedo, A. (2016). Exposure to violence predicts poor educational outcomes in young children in South Africa and Malawi. International health, 8(1), 36-43.
14. Makhosazana Kubeka, A. (2008). Exposure to violence at home: A qualitative exploration of experiences and perceptions of black adolescents in South Africa. South African Review of Sociology, 39(2), 282-300.
15. Kieselbach, B., Kress, H., MacMillan, H., & Perneger, T. (2021). Prevalence of childhood exposure to intimate partner violence and associations with mental distress in Cambodia, Malawi and Nigeria: A cross-sectional study. Child Abuse & Neglect, 111, 104807.
16. Asagba, R. B., Noibi, O. W., & Ogueji, I. A. (2022). Gender differences in children’s exposure to domestic violence in Nigeria. Journal of child & adolescent trauma, 15(2), 423-426.
17. Kishor, S., & Johnson, K. (2005). Profiling domestic violence: a multi-country study. Studies in Family Planning, 36(3), 259-261.
18. Mrug, S., & Windle, M. (2010). Prospective effects of violence exposure across multiple contexts on early adolescents’ internalizing and externalizing problems. Journal of Child Psychology and Psychiatry, 51(8), 953-961.
19. Davis, R. (2010). Domestic violence�related deaths. Journalof aggression, conflict and peace research, 2(2), 44-52.
20. Miller, D. H., Greene, K., Causby, V., White, B. W., & Lockhart, L. L. (2014). Domestic violence in lesbian relationships. In Intimate Betrayal (pp. 107-127). Routledge.
21. Dyson, J. L. (1990). The effect of family violence on children's academic performance and behavior. Journal of the National Medical Association, 82(1), 17.
22. Estefan, L. F., Armstead, T. L., Rivera, M. S., Kearns, M. C., Carter, D., Crowell, J., ... & Daniels, B. (2019). Enhancing the national dialogue on the prevention of intimate partner violence. American journal of community psychology, 63(1-2), 153-167.
23. Jaffe, P. G., Crooks, C. V., & Poisson, S. E. (2003). Common misconceptions in addressing domestic violence in child custody disputes. Juvenile and family court journal, 54(4), 57-67.
24. Turunç, G., & Kisbu-Sakarya, Y. (2022). Parents’ Attitudes Toward Domestic Violence as a Risk Factor for Early Childhood Development: Testing an Actor–Partner Interdependence Model Using UNICEF MICS. Journal of interpersonal violence, 37(23-24), NP21476-NP21501.
25. Gedara, N. J. J. H. (2016). Domestic violence and children: Raising awareness of school personnel responsibilities (Doctoral dissertation).
26. Bourdillon, M. (2014). Children’s work. Handbook of child well-being: Theories, methods and policies in global perspective, 821-861.
27. YUSUF, F. A. (2017). Impacts of domestic violence against women on students’ academic performance as perceived by primary school teachers in Nigeria. Educational Journal of the University of Patras UNESCO Chair.
28. Harding, L. (2001). Children's quality of life assessments: a review of generic and health related quality of life measures completed by children and adolescents. Clinical Psychology & Psychotherapy: An International Journal of Theory & Practice, 8(2), 79-96.
29. Grip, K. K., Almqvist, K., Axberg, U., & Broberg, A. G. (2014). Perceived quality of life and health complaints in children exposed to intimate partner violence. Journal of Family Violence, 29(6), 681-692.
30. Edleson, J. L., Johnson, K. K., & Shin, N. (2007). Children’s exposure to domestic violence scale. St. Paul: Minnesota Center against Domestic Violence.
31. Edleson, J. L., Ellerton, A. L., Seagren, E. A., Kirchberg, S. L., Schmidt, S. O., & Ambrose, A. T. (2007). Assessing child exposure to adult domestic violence. Children and Youth Services Review, 29(7), 961-971.
32. Edleson, J. L., Shin, N., & Armendariz, K. K. J. (2008). Measuring children's exposure to domestic violence: The development and testing of the Child Exposure to Domestic Violence (CEDV) Scale. Children and Youth Services Review, 30(5), 502-521.
33. Ravi, K. E., & Tonui, B. C. (2020). A systematic review of the child exposure to domestic violence scale. The British Journal of Social Work, 50(1), 101-118.
34. Akpunne, B. C., Ayodele, A. O., Babatunde, S. I., Ogunsemi,J. O., & Ndubisi, C. D. (2020). Associations between exposure to domestic violence, depression and cyberbullying among secondary school adolescents. European Journal of Medical and Health Sciences, 2(4).
35. IliÄ?, I., ŠipetiÄ?-GrujičiÄ?, S., GrujičiÄ?, J., ŽivanoviÄ? MačužiÄ?, I., KociÄ?, S., & IliÄ?, M. (2019). Psychometric properties of the world health organization’s quality of life (WHOQOL-BREF) questionnaire in medical students. Medicina, 55(12), 772.
36. Akpa, O. M., & Fowobaje, K. R. (2018). The Factor Structure of the Adapted WHO Quality of Life BREF questionnaire in a sample of adolescents in Nigeria. Archives of basic and applied medicine, 6(1), 35.
37. Hawthorne, G., Herrman, H., & Murphy, B. (2006). Interpreting the WHOQOL-BREF: Preliminary population norms and effect sizes. Social indicators research, 77(1), 37-59.
38. Akpa, O. M., & Bamgboye, E. A. (2015). Correlates of the Quality of life of Adolescents in families affected by HIV/ AIDS in Benue State, Nigeria. Vulnerable children and youth studies, 10(3), 225-242.
39. DuPaul, G. J., Rapport, M. D., & Perriello, L. M. (1991). Teacher ratings of academic skills: The development of the Academic Performance Rating Scale. School psychology review, 20(2), 284-300.
40. Akiri, A. A., & Ugborugbo, N. M. (2009). Teachers’ effectiveness and students’ academic performance in public secondary schools in Delta State, Nigeria. Studies on Home and Community science, 3(2), 107-113.
41. Quadri, M. F. A. Q., El-Kordy, M. S., Moukhyer, M. E., Mukhayer, A., & Ahmad, B. (2017). Psychometric Properties and Validation of the Arabic Academic Performance Rating Scale. Education Research International, 2017(1), 1504701.
42. Goldberg, P. (1972). The detection of psychiatric illness by questionnaire. Maudsley monograph.
43. Goldberg, D. P., & Hillier, V. F. (1979). A scaled version of the General Health Questionnaire. Psychological medicine, 9(1), 139-145.
44. Goldberg, D. P., Gater, R., Sartorius, N., Ustun, T. B., Piccinelli, M., Gureje, O., & Rutter, C. (1997). The validity of two versions of the GHQ in the WHO study of mental illness in general health care. Psychological medicine, 27(1), 191-197.
45. Akadri, A., Adepoju, A., Bamidele, O., Oluwole, T., Sodeinde, K., & Abiodun, O. (2024). Mental health distress and associated factors among HIV-positive adolescents attending ART Clinics in Nigeria. Global Pediatrics, 9, 100180.
46. Aloba, O., Opakunle, T., & Ogunrinu, K. (2019). Alternative models examination and gender measurement invariance of the 12-item general health questionnaire among Nigerian adolescents. Psychiatry Investigation, 16(11), 808.
47. Okon, I., & Njoku, C. G. (2017). Evaluation of domestic access to pipe-borne water in Calabar Metropolis, Southern Nigeria. Open Access Library Journal, 4(12), 1-21.
48. Michael, G., Usang, E., Nelson, C., Etim, J., Onah, A. I., & Chukwudi, E. (2014). The Effect of Population Explosion on Family Standard of Living in Calabar, Nigeria. European Scientific Journal, 10(20).
49. Kieselbach, B., Kimber, M., MacMillan, H. L., & Perneger, T. (2022). Prevalence of childhood exposure to intimate partner violence in low-income and lower-middle-income countries: A systematic review. BMJ open, 12(4), e051140.
50. Nouer, S. S., Mackey, S. N., Tipton, N. G., Miller, A. C., & Connor, P. D. (2014). Identifying predictors for children witnessing intimate partner violence. Journal of family violence, 29(6), 675-679.
51. Karanja, D. N. (2019). Effects of domestic violence on students’ academic performance in public day secondary schools in Tetu Sub County, Nyeri County, Kenya. Published online.
52. Omolabake, A. O., & James, B. (n.d.). Influence of Identified Variables of Domestic Violence on Academic Achievement of Secondary School Students in Niger State, Nigeria.
53. Meltzer, H., Doos, L., Vostanis, P., Ford, T., & Goodman, R.(2009). The mental health of children who witness domestic violence. Child & Family Social Work, 14(4), 491-501.
54. Fantuzzo, J. W., & Mohr, W. K. (1999). Prevalence and effects of child exposure to domestic violence. The future of children, 21-32.
55. Rossman, B. B. (2001). Longer term effects of children'sexposure to domestic violence.
56. Olofsson, N., Lindqvist, K., Shaw, B. A., & Danielsson, I. (2012). Long-term health consequences of violence exposure in adolescence: A 26–year prospective study. BMC public health, 12(1), 411.