Prerpints.org logo
Preprint
Article

Psychological distress in Low Income and Economically Marginalized (LIEM) populations in India: Protective and Risk factors.

Altmetrics

Downloads

95

Views

43

Comments

0

A peer-reviewed article of this preprint also exists.

Submitted:

12 December 2023

Posted:

12 December 2023

You are already at the latest version

Alerts
Abstract
Studies at the juncture of development economics and public health host considerable responsibility in addressing inequality and related mental health distress. Mental health care in economically marginalized populations requires depicting the linkages between socioeconomic status and psychological distress. In the present work, a sequential mixed-methods design was used to study 190 people in such communities in India. Gender-dependent psychological distress was found according to Kessler Psychological Distress Scale (K-10) with moderate distress in women (M= 26.30, SD= 9.15) and mild distress in men (M= 21.04, SD= 8.35). Regression analysis indicated that gender significantly predicted psychological distress, followed by age, marital status, and education of the head of the family. The Interpretative Phenomenological Analysis of semi-structured interviews to six women who scored the highest on the distress scale unveiled three master themes: (1) Manifestation of Psychological Distress, (2) Contextual Challenges, and (3) Sources of Strength and Resilience. Overall, participants reported lack of resources, community violence, gender discrimination, and widespread substance use as major contributors to the ongoing distress. These findings can pave the way for future studies to expand beyond independent economic indicators and curate clinical interventions for culturally competent mental health care.
Keywords: 
Subject: Public Health and Healthcare  -   Public Health and Health Services

1. Introduction

Psychology as a discipline has been at the receiving end of considerable backlash because of research largely affected by socio-demographic and socioeconomic bias and the assumption that ‘one model fits all'. Thus, studies based on Western populations—educated, industrial, rich, and democratic people (the so-called WEIRD populations) [1]—may suffer from a restrained cultural relevance of their research findings [2]. Therefore, to make psychology more inclusive, the APA Guidelines for Psychological Practice for People with Low-Income and Economic Marginalization (LIEM) were introduced in 2019 [3]. LIEM refers to ‘an umbrella term encompassing limited financial resources and marginalization related to class'. Moreover, mental health professionals have been criticized for being underrepresented in addressing the growing global social inequalities [4]. Therefore, it is imperative to document the experiences of communities that have been marginalized and deserve the requisite clinical attention. This calls for increased access to quality mental healthcare and culturally appropriate training through a socially inclusive lens. Moreover, without taking into account the myriad of factors, mental health-care ceases to work effectively with a diverse set of the population [5].
Research across various disciplines has shown the implications of economic marginalization on health and well-being [6,7]. There is evidence that low-income spaces are characterized by rampant poverty, poor infrastructure, community violence, and limited career opportunities that further exacerbate the vulnerability to developing mental health issues [8]. In India (the site of the present research), the severity of the mental health burden is alarming and calls for an urgent need for action [9]. According to a report up to 2017, one in every seven people in India is affected by mental illness, ranging from mild to severe symptoms [10]. It is imperative to observe that human beings interact in multiple-locked, interconnected systems (age, gender, social class, race/ethnicity, caste, migration, etc.) and power hierarchies. The complexities of these social identities and power structures generally ascertain the experiences of oppression and privileges followed in society [11]. Intersectional inequalities are evident in healthcare utilities and are estimated to be significant in mental health outcomes [12]. Moreover, unemployment in India has risen considerably due to the COVID-19 pandemic, as around 1.5 million Indians have lost their jobs during this period [13]. Consequently, systematic inequalities have only widened the disparities, with limitations in access to quality mental healthcare. Thus, it is important to study socioeconomic status (SES) across age, gender, caste, and class in a culturally diverse country like India. The present study is a modest attempt in this direction, as it aims to explore the linkages between socioeconomic status (SES) and psychological distress among LIEM populations in India.
Psychological distress is considered an important indicator of any pathological condition. It refers to the "general concept of maladaptive psychological functioning in the face of stressful life events" [14]. Psychological distress is a transient and modifiable non-specific factor of stress, anxiety, and depression that can lead to impaired mental health in individuals [15]. It can be understood as a form of emotional suffering with somatic and psychological symptoms bearing an effect on social functioning [16]. Psychological distress is more evident in highly vulnerable socioeconomic populations and those individuals facing social inequalities (Santana et al., 2021). The APA resolution on Poverty and Socioeconomic Status (2000) posits that the mental health risk is higher for racial and ethnic minorities, older adults, refugees and immigrants, persons with disabilities, individuals identifying as LGBTQIA, single mothers, foster children, and people with mental illness [4,17].
Low socioeconomic status (SES) is highly associated with frequent mental health issues as compared to those with high SES (Safran et al., 2009). There is evidence that poverty and income inequality are not only associated with psychological distress but also with other mental illnesses, including depression [18], suicide [19], and schizophrenia [20]. It would be interesting to report that "the study of poverty and mental health in low and middle-income countries is relatively young" [21], and to the best of our knowledge, systematic measurement of psychological distress in economically marginalized communities has been a relatively less explored area in the Indian context as well [22].
Socioeconomic Status (SES) has been an elusive term in psychological literature, as both theoretical and methodological issues are complicated [23]. Socioeconomic status (SES) is a critical aspect of other social science research as well, particularly in the fields of economics and sociology [24]. While the terms social position, social class, and SES are often used synonymously, there are nuanced distinctions between them. This lack of conceptual clarity and limited focus given to SES within psychology has restricted the scope of application of research findings [3]. In many low and middle-income countries (LMICs) including India, there has been a focus on objective measures of SES like poverty [25]. However, other studies have also used multiple SES indicators like household and education [26] and property ownership [27]. APA Taskforce on Socioeconomic Status (2007) suggested a ‘triumvirate’ measure of Income, Education, and Employment as the most appropriate measure of SES [4]. The income indicator is related to the average total household income of the family, the education index is reflected in the highest level of education attained by the head of the family, and the employment index is the current status of work employment and associated job roles for the family members. Each of these three measures is multidimensional in itself, and thus choosing the most appropriate indicator should depend on the outcome of interest.
Considering India’s social stratification and cultural diversity, it’s imperative to focus on other sociodemographic factors, and therefore any analytical framework needs to incorporate caste as well for a better understanding [28] Caste hierarchies and stratification influence social roles and occupations [29], along with deeply entrenched discrimination and social exclusion [30]. Social discrimination, such as caste discrimination, was found to be related to poor mental health as well [31,32]. While caste continues to be a pervasive issue, the empirical data on caste inequalities and mental health has found little representation in India [22,30]. Therefore, there is a need for psychological research and practice to be informed from the perspective of the marginalized communities that are under-represented, under-served, and most importantly, severely distressed.
Thus, the present research aims to explore the relationship between various indices of SES and psychological distress in LIEM communities situated in the National Capital Region of Delhi, India (NCR). The study used a sequential mixed-methods design. Here, a mixed-method approach will not only facilitate our understanding of the nature of distress and but also help us delve deep into the precedents and the lived experiences of the people.

2. Materials and Methods

2.1. Sample

Data was collected from 190 respondents aged above 18 years from LIEM communities in New Delhi, using purposive sampling. Participants were informed about the purpose of the study and the informed consent was obtained before conducting the data collection. The demographic details of the sample are illustrated in the result section.
For the qualitative investigation, 10 participants who scored highest on Psychological distress scale (Kessler-10) [33] were selected, of whom 6 agreed to the interviews. All six of the selected participants for interviews were women and they belonged to ST/SC backgrounds, the details of which are presented in the result section.

2.2. Measures

Socioeconomic status: For measuring SES, the Modified Kuppuswamy SES scale 2021 was used [34]. It was created by Kuppuswamy in 1976, and modified in 2021 to accommodate the socioeconomic changes in the country over the years. It consists of three indices of SES such as education, employment, and income. Moreover, a total score on SES can also be obtained by combining all three indices.
Caste: In the socio-demographic form, the subcategories for caste have been taken from Pareek’s Revised Scale-2019 [35]. It measures caste in the categories of: general caste, agriculture caste, artisan caste, lower caste, schedule caste, and schedule tribe.
Psychological distress: Kessler Psychological Distress Scale (K-10) was used to measure psychological distress [33]. It consists of ten items with a 5-point scale, ranging from ‘None of the Time’ to ‘All of the Time’. The items of the scale included, “In the past 4 weeks, how often did you feel depressed?”, “In the past 4 weeks, how often did you feel nervous? The Cronbach Alpha value for K-10 was found to be 0.90 on the current sample.

2.3. Procedure

Before conducting the main study, K-10 was pilot tested on 30 respondents from one of the LIEM communities located in the NCR, India. Cronbach alpha value was found to be 0.83. An a priori power analysis using G*Power version 3.1.9.4 software (Faul, 2019) was used to estimate the sample size to detect a medium effect, f2= .15, with 80% power while using linear multiple regression (fixed model & R2 deviation from zero) at an alpha level of 0.05. The estimated sample size was to be 146 and therefore the actual sample size of the study (N= 190) is adequate to test the hypotheses of the study. Before the administration, an informed consent form was signed, followed by the demographic form comprising age, gender, religion, caste, marital status, hometown, etc.
Semi-structured interviews were conducted on six women respondents (as mentioned earlier). Informed consent was signed by the participants prior to the interview. Interviews were conducted face-to-face and were recorded using a simple telephonic recorder. The interviews consisted of ten questions which took about 25-30 minutes for each participant. The interviews were transcribed verbatim for the analysis.

2.4. Design and Analytical Plan

The study employed a sequential mixed-method design [36]. The quantitative data were analyzed using descriptive statistics, correlation, and multiple linear regression. Qualitative interviews were analyzed using the Interpretative Phenomenological Approach (IPA) by Smith et al., (2009) [37]. IPA is a qualitative approach, at the core of which are the lived experiences of a particular phenomenon. According to Creswell (2013), “phenomenological study describes the common meaning for several individuals of their lived experiences of a concept or a phenomenon” (p. 76) [38]. IPA was chosen for this study given the sensitivity of the context and the rich narratives. While other qualitative approaches are focused on building themes from the data, IPA allows the experiences to be at the forefront with a detailed picture of the phenomena [39].

3. Results

3.1. Descriptive Statistics and Correlation

Table 1. Sample descriptive.
Table 1. Sample descriptive.
Variable Level Frequency Percentage
Age 18-76
Mean- 32.8, SD-12.43		 - -
Gender Male
Female		 93
97		 48.9
51.1
Religion Hindu
Muslim
Other		 175
14
1		 92.1
7.4
.5
Caste a Scheduled Caste
Lower Caste
Artisan Caste
Agriculture Caste
General Caste		 117
23
8
11
22		 61.6
12.1
4.2
5.8
11.6
Marital Status
 Married
Unmarried		 150
40		 78.9
21.1
Education
of the Head
of the Family b
 IlliteratePrimary School
Middle School
High School Certificate
Intermediate/Diploma
Graduate		 38
38
74
29
4
7		 20
20
38.9
15.3
2.1
3.7
Occupation of
The Head of
The Family b,c
Unemployed
Low-skilled jobs
Medium-skilled jobs
High-skilled jobs		 22
111
44
13		 11.6
58.4
23.1
6.9
Income of
the Head of
the Family (in Rupees) b
<6174
6,175-18,496
18,496-30,830
30,830-46,128
46,129-61,662		 24
154
8
3
1		 12.6
81.1
4.2
1.6
.5
a Pareek (2019), b Saleem & Jan (2021), c (ILO, 2019).
In the present study, 73.7% of the participants belonged to ST/SC and Other Backward Caste (OBC) backgrounds, and around 4.2 and 5.8 belonged to artisanal and agricultural castes, respectively. Only 11.6% of the participants belonged to the general category (higher caste).
Table 2. Demographic details of the interview participants.
Table 2. Demographic details of the interview participants.
Name Age Religion Caste a Education b Marital Status Employment b Income Level a (Rs)
Mrs. M 55 Hindu SC/ST Illiterate Married Unemployed 6175-18,496
Mrs. K 45 Hindu SC/ST Illiterate Married Unemployed 6175-18,496
Mrs. S 34 Hindu SC/ST Illiterate Married Unemployed 6175-18,496
Mrs. R 45 Hindu SC/ST Illiterate Widow Unemployed 6175-18,496
Mrs. T 37 Hindu SC/ST Illiterate Married Unemployed <6174
Mrs. V 45 Hindu SC/ST Illiterate Married Unemployed 6175-18,496
a Pareek (2019), b Saleem & Jan (2021). ECB Exchange rates: 2023-12-11, 6175-18,496 Rs = 68.85-206.24 €; 74.07-221.86 USD.
For the qualitative participants, 10 individuals who scored highest on the distress scale were selected for the interview. All ten participants were women and from SC/ST caste. Out of which, 4 participants refused to be the part of the interview schedule.
The study variables’ mean, standard deviation, skewness, kurtosis, and Pearson correlation for the study variables are presented in Table 3. The normality of data was obtained as the skewness and kurtosis values were within the prescribed range of -2 to +2. However, the kurtosis value was found to be 15.79 for the family income, this is because most of the respondents scored towards the lower side of the income range as they belonged to the LIEM population.
Table 3. Correlation coefficient values for all the study variables.
Table 3. Correlation coefficient values for all the study variables.
Variable Mean (SD) Skewness Kurtosis 1 2 3 4 5 6 7
1. Age 32.85 (12.43) .66 .06 1 -.15* -.43** -.30** -.03 .04 .20**
2. Gender 1.51 (.50) -.04 -2.0 -- 1 -.06 .50 -.09 -.13 .29**
3. Marital Status 1.21 (.41) 1.43 .05 -- -- 1 .22** .10 .07 .01
Head of the Family
 4. Education 		2.70 (1.22) .48 .27 -- -- -- 1 -.04 -04 -.16*
 5. Occupation 2.99 (2.03) 1.52 1.52 -- -- -- -- 1 .23** -.08
 6. Income 1.97 (.56) 2.33 15.79 -- -- -- -- -- 1 -.09
7. Psychological distress 23.73 (9.13) .53 -.62 -- -- -- -- -- -- 1
The results showed a significant and positive correlation between gender and psychological distress (r =.29, p < .01), implying that females experienced more psychological distress than men. We can also see a significant and positive correlation between age and psychological distress (r = .20, p < .01). There is a significant and negative correlation between the education of the head of the family and psychological distress (r = - .16, p < .05).

3.2. Multiple Regression Analysis

As presented in Table 4, about 19% (F = 7.23, p < .01) of the variance in psychological distress can be explained with the help of predictors i.e. gender, age, marital status, occupation of the head of the family, education of the head of the family, and income of the family. The effect size for the same was reported to be moderate (f2 = 0.24), as per Cohen’s (1988) guidelines. Moreover, as indicated by Table 4, gender (β = .34, t = 4.95, p < .001) emerged as the most important predictor, where females were found to report more psychological distress than males. This was followed by age (β = .29, t = 3.79, p < .001), where increased psychological distress was reported with an increase in age, marital status (β= .20, t = 2.67, p < .01), where unmarried respondents reported higher distress, and finally education levels (β = -.14, t = -2.03, p < .05), where lower levels of education of the head of the family predicted higher psychological distress.
Table 4. Multiple regression of variables predicting psychological distress.
Table 4. Multiple regression of variables predicting psychological distress.
Variable B Std. Error β t p
Age .21 .06 .29 3.79 *** < .001
Gender 6.2 1.2 .34 4.95 *** < .001
Marital Status 4.5 1.7 .20 2.67 ** .008
Head of the family
 Education -1.07 .53 -.14 -2.03 * .04
 Occupation -.23 .31 -.05 -.76 .45
 Income -1.11 1.12 -.07 -.99 .32
R= .44, R2= .19, F (6,183) =7.23, p < .01.

3.3. Qualitative Analysis

In this section, the IPA analysis of the transcripts is presented. The analysis yielded three master themes and seven sub-themes that provided insight into the lived experiences of participants as it pertains to their psychological distress.
Table 5. Overview of the master themes and subordinate themes.
Table 5. Overview of the master themes and subordinate themes.
1. Manifestation of psychological distress
a. Physiological disturbances and bodily concerns
b. Emotional and psychological disturbances
2. Contextual challenges
 a. Experiences of living in abject poverty
b.  A spectrum of violence and traumatic incidents
 c. Lack of social support systems
3. Sources of strength and resilience
a. Religion as means of coping
b.  Family as a ray of hope

3.3.1. Master Theme 1: Manifestation of psychological distress

This theme explains how symptoms of psychological distress were manifested among the participants in both physical and emotional forms.
  • 3.3.1.a. Physiological disturbances and bodily concerns: The disturbances in sleep patterns and lack of appetite were reported by four out of the six participants. They described waking up in the middle of the night, due to a sense of restlessness. All six participants also reported extreme fatigue and exhaustion.
Mrs. R has complained of less than adequate sleep in the last two years. She worried excessively while being preoccupied with stressors at night and was also recommended by doctors to relieve some of her tension and anxiety.
  • Mrs. R: “ I can’t sleep properly at night, everyone says that I think too much at night, I feel extremely restless at night and feel like running away from home.”
Mrs. V gave a detailed explanation of her chronic stomach pain, and while she had consulted several doctors for the same, no significant medical reasons were identified, and no sense of relief was observed.
  • Mrs. V: “When I am stressed or worried, I can’t eat. I always have pain in my stomach, but no medicines have helped.”
  • 3.3.1.b. Emotional and psychological disturbances: The participants expressed excessive worry about the future and were fearful of losing their jobs during the COVID pandemic.
Mrs. T was also constantly worried about rationing the food and if her husband would be able to earn enough money to pay the gas bill.
  • Mrs. T: “The whole month I worry if we can buy enough ration. COVID brought many problems, and we were constantly worrying if someone can at least give Rs. 10-20 to us.”
In addition to this, one could also observe persistent feelings of sadness, and most importantly frequent crying spells. At some point in the interview, all participants either softly teared up with the sudden flash of the traumatic incidents or cried profusely, holding the interviewer’s hand for support. Mrs. S teared up at the mention of her childhood, hoping that her children are not put through the same ordeals.
Mrs. M cried twice during the interview when remembering her deceased son. She also explained her initial symptoms of depression, where she would not move from her place, barely sleep or eat at all, and would just cry at the sight of her daughter-in-law.
  • Mrs. M: “I was broken from inside and I had left everything and would just lie on this sofa the whole day crying. My son would say that mother you have gone crazy, what will we do, we have nobody except you.

3.3.2. Master theme 2: Contextual challenges

This theme describes the experiences and challenges of living in an urban slum which are marked by daily physical and social difficulties.
  • 3.3.2.a Experiences of living in abject poverty. The participants explained their daily hassles and the general environment as being riddled with extreme poverty, low education, and limited resources. All six women gave a detailed explanation of their personal experiences of struggling to make ends meet. All the participants had not received formal education and had to drop out of primary school due to financial constraints.
Mrs. V mentioned that being unemployed was the biggest worry in her life and that the COVID-19 pandemic exacerbated the challenges for them as a family.
  • Mrs. V: “We both lost our jobs, our children are living hand to mouth, not getting work during the Lockdown has made things even more difficult. “
Mrs. K also expressed their desire to study and said that they would not have suffered so much had they completed their education.
  • Mrs. K: “I have not studied at all, neither was I sent to school, nor did I go myself. My parents were extremely poor, and we were struggling as a family of eight. None of the children have gone to school, only my parents earned a livelihood, that too through boot polishing.”
In addition to the limited access to resources, all the women respondents complained of an unsafe environment owing to widespread substance use and alcoholism in the community.
  • Mrs. S: “The biggest problem here are the alcoholics, as they are ruining the future of the children, everyone has access to substances right from small children to older men.”
  • 3.3.2.b A spectrum of violence and traumatic incidents. A common element in all the interviews were the acts of violence. It ranged from verbal and physical abuse to caste and gender-based violence. The women reported young boys using abusive language and the older men, including their husbands, engaging in domestic violence due to chronic alcoholism.
Mrs. R explained how before the death of her husband, there were regular verbal and physical incidents between the couple. Mrs. M also narrated the whole story of how her young son was killed in a caste-based violence. Despite overt resistance by the two families, the boy and girl had run away from home and gotten married. They both were badly beaten multiple times, along with public shaming and psychological abuse.
  • Mrs. M: “Everyone said that my son has flown away with the girl. Both of them were badly beaten, they were punched in the stomach, I think some 12 stitches were needed because they had hit my son with iron rods. Imagine such brutality only because of caste differences.”
Mrs. V went on to explain how her young daughter was allegedly burned in her marital home. The girl’s face was pushed onto a burning stove, resulting in second-degree burns. Mrs. V was worried and tensed for her three unmarried daughters as despite giving a huge amount of dowry in the elder girls' marriages, such harrowing incidents continued to plague her family.
  • Mrs. V: “Her (the daughter) eyes and whole face were swollen, and her cheeks were burnt and were sagging profusely. Thankfully, her ears were saved but the first time that I saw her I almost fainted.”
  • 3.3.2.c Lack of social support systems. This sub-theme is concerned with the general social environment, where most of the participants preferred to keep limited interactions with other community members, in addition to a perceived lack of sense of belonging to the community.
Mrs. R reported how most people are ill-mannered and that her family can only vouch for themselves.
  • Mrs. R: “We can’t change people, best is to keep to ourselves, and this is a sign of our helplessness, where can poor people go. People fight here for basic water and necessities. I am scared that no one should get killed in this process.”
Mrs. S does not mingle with other community members, nor does she allow her daughters to do.
  • Mrs. S: “I don’t like talking to anyone, I only stay at home. I don’t share my life with others in the community and prefer to be by myself as they will only gossip. There are some good people but mostly I believe people are not good-natured.”

3.3.3. Master theme 3: Sources of strength and resilience

This theme explores the sources of resilience to sustain and persist through the challenges. Their belief in God and religion was found to be an important factor, along with inner resilience as means to keep moving ahead for the survival of their families.
  • 3.3.3.a Religion as means of coping: Three out of the six participants described their immense devotion to God for helping them navigate the challenges of their lives.
Mrs. M was completely immersed in bhakti and devotion through prayers and fasting to cope with the loss of her son. She even mentioned that only God can help her get through her miseries.
  • Mrs. M:” I want that god takes me into his abode, maybe my life will be successful then. I only recite his name every day and he is the only one who has given me the strength to deal with such a loss. I did not have anywhere to go but him.”
Mrs. T expressed the same sentiment, iterating that she is not alone in this world and only God can give her the strength to fight each day.
  • Mrs. T: “This is the rule of life, sometimes sadness and sometimes happiness, God will give you the strength to deal with these challenges.”
  • 3.3.3.b Family as a meaning of life: They expressed positive emotions and a sense of hope for what the future holds for them. This was partly true because of their families, and most importantly, the children, who continue to be their reason for survival. All of them believed that only quality education could bring some respite from their ongoing struggles and systematically uplift them from poverty.
  • Mrs. K: “I have seen my mother fight the same way and I have seen the struggles up close. This has given me the strength to fight back and help my children.”

4. Discussion

An investigation was conducted to explore the association between various SES indicators and psychological distress in LIEM communities. The findings were in terms of risk and protective factors.

Risk factors

The study aimed to explore psychological distress among LIEM populations of Delhi-NCR, India using a sequential mixed-methods design. The results of the multiple regression analysis showed that gender was the most important predictor of psychological distress, with women experiencing more psychological distress than men. As per the cut-off given by Kessler et al. (2003), women in the present study experienced moderate distress (M= 26.30, SD= 9.15), as compared to men experiencing mild distress (M= 21.04, SD= 8.35). This resonated with the findings of a study by Coffey et al., (2021) in three states of India (Maharashtra, Bihar, and Jharkhand), where women were found to have experienced more distress than men [40]. Moreover, gender has also been found to be the second highest factor influencing mental health for women in slums and low-income spaces [41]. These findings can be attributed to the rigid gender roles and cultural norms that operate for women in many marginalized communities’ including India [42]. India is also riddled with gender inequalities, that act as barriers for young girls and women to experience the same privileges as men concerning health, education, employment, and general dignity [43]. Therefore, they are at a higher risk of developing mental health illnesses [44], especially in low-income and economically marginalized communities.
Results also revealed that the age, education, and marital status of the respondents explained psychological distress significantly. Older participants were found to be more distressed. In a study conducted by Joshi et al. (2003), as many as 66% of the older people in the sample were seen to be physically and psychologically distressed [45]. On exploration of the psychosocial variables influencing mental distress, these findings can be attributed to an increase in family responsibilities and a lack of healthcare facilities. Additionally, it has been seen that mental health distress is likely to increase with age, with low socio-economic background acting as a major mediating factor [46]. Taking gender into account, it has been seen that in the Indian context, older women have been found to be more distressed than men in the rural areas of Northern India [47]. There is also evidence that age and educational status had significant effects on the psychological impairments for older women in the slums of Kolkata, India [48]. In the present study as well, lower education levels of the head of the family predicted psychological distress. There is evidence that higher education and having a job positively correlate with mental health in the US and South African samples [49]. Moreover, limited educational qualification has been associated with general high distress and low well-being [50], and the dementia risk [51]. Within Indian samples, it has been reported that mental health outcomes and vulnerability to developing common mental disorders like depression and anxiety are associated with low education [46].
The results also revealed that married respondents experienced less distress than single respondents. A Norway-based sample reported significantly better mental health scores for married people than unmarried people [52]. In the Indian context, recent studies on health workers during COVID revealed that older women and unmarried people reported high mental health stress [53] and high risk for anxiety and depression [54]. Similarly, widows who are older adults and lack socio-economic resources have been found to be at increased risk of developing depression and deteriorating health conditions [46]. Therefore, a multifaceted approach is needed to understand the better mental health outcomes of married individuals [55]. Factors like socioeconomic conditions, demographic factors such as age, and gender, psychological resources like social support might moderate the relationship between marital status and psychological distress.
The qualitative data analysis was carried out to explore the lived experiences of the participants with regard to psychological distress. All the participants belonged to economically marginalized families with income levels below $2.15 per day, as per the International Poverty line for the year 2017 cut-off values [56]. There is a strong relationship between poverty and common mental disorders in low and middle-income countries [57] There is evidence that being exposed to chronic poverty-related stressors leads to physical and mental health problems [58] like experiencing symptoms of depression, anxiety, suicidality, sleep problems, and somatization [59]. Subbaraman et al. (2014) found that mental health disorders like depression and anxiety were related to poverty-related factors in the slums of Mumbai, India [60]. It was also observed that with the sudden flash of a traumatic incident, four out of six women in the study showed frequent crying spells and unusual sadness, along with bodily complaints. This can be supported by the community mental health statistics that point toward lower mental health well-being for women [61,62,63].
In the present study, close to two two-thirds participants (71% to be precise) belonged to Schedule Caste community and this is a reflection of the uneven distribution of wealth in India [64]. The mental health outcomes of lower caste communities in themselves bring forward staggering results that warrant a community-sensitive approach, taking into account the years of collective trauma [65]. For women, this represents a gendered vulnerability to violence and lower mental health well-being, owing to class and caste-based inequalities [30,66] For instance, Prost et al. (2012) found that women belonging to Schedule tribes or Schedule caste backgrounds reported higher psychological stress as opposed to the higher-caste women [67]. Intersectionality highlight how various societal factors such as gender, age, class, and caste intersect and create a complex web of health inequalities. These inequalities are not isolated but rather reinforce one another, leading to a more entrenched and pervasive system of inequality, thus, in turn, having a multifaceted impact on mental health.
The daily stressors in the community revolve around the plenitude of violent acts leading to personal and collective trauma. The participants narrated incidents of domestic violence, caste and gender-based violence, and general community aggression. Women in low-income communities also experience high rates of structural and gender-based violence (GBV), besides other sources of stressors. Specifically, Indian women from lower castes, with limited education, are vulnerable to GBV, owing to a lack of financial stability. [68,69]. Another study by Datta and Satija (2020) throws light on gender and caste violence in slums, which they find to be normalized by layers of traditional and newer forms of discrimination like dowry, son preference, and overt forms of physical and sexual violence [70]. Research has repeatedly shown a significant connection between alcoholism and domestic violence [71,72,73] as well as the negative impact that violence can have on mental health [74,75,76]. By recognizing these interconnected issues, we can prevent further harms and promote overall well-being. While social support has been considered a protective factor, the social environment of the participants in the present study did not provide a sense of support, comfort, or belongingness. These findings are similar to Mumbai-based slums that reported around 33% of women not experiencing any social support [76]. Moreover, low social and family engagement were found to be predictors of depression for older adults in rural Allahabad [47].

Protective factors

Religiosity

Despite life’s daily ordeals, all the participants displayed resilience in dealing with the above-mentioned stressors. Religiosity emerged as a means to cope with adversities for many participants. Research has indicated that religious beliefs and practices are associated with lower anxiety and depression symptoms [78] and overall greater psychological health [79,80].
  • Children and family as meaning of life:
The well-being of their family and the educational pursuits of their children propel them to move forward in life and hope for a better future. Not only does family coping facilitate the management of stressors like job loss, financial crisis, and health issues, [81] but it also creates opportunities to break the cycle of intergenerational poverty. The children act as a symbol of meaning-making, a ray of hope to overcome their existing financial hardships, and most importantly, they help overcome economic constraints. These findings can find relevance in family-based psychosocial interventions aimed at fostering resilience in low-income settings.
Limitations: The sample adequacy was established before the data collection; the study was limited to Delhi-NCR region of India. Therefore, future studies are encouraged to expand across the social-regional diversity of India. Secondly, the in-depth interviews were centered on the lived experiences of women participants who reported highest psychological distress, thus a deeper gender perspective may not have been tapped. Thirdly, the study's findings on the relationship between caste, socioeconomic status, and mental health issues were limited due to the skewed data as 71% belonged to the Scheduled caste communities, which prevented a comprehensive understanding of the systemic caste and class stratification. Further investigation is required to establish the linkages among these variables. Finally, given the mental hygiene movement [82] and the need for health interventions to expand their applicability of intersectionality principles, [83] notably, the studies at the juncture of development economics and public health host considerable responsibility in addressing inequality and related mental health distress.

5. Conclusion

The results of the quantitative investigation revealed that gender, age, and educational status of the head of the family significantly predicted psychological distress. The qualitative inquiry furthered our understanding of the nature of distress, the context, and the associated symptomology, in addition to the varying coping mechanisms. Moreover, it did so under the lens of a triple vulnerabilities of low income, illiteracy, and gender. This highlighted the intersectionality of the myriad factors influencing SES and psychological distress.

Author Contributions

Conceptualization, D.S., J.S., S.N., L.G.-L., and M.G.S; methodology, D.S., S.N., and M.G.S.; software, D.S., and M.G.S.; validation, D.S., and M.G.S.; formal analysis, D.S.; investigation, D.S., and M.G.S.; resources, D.S., and J.S.; data curation, D.S.; writing—original draft preparation, D.S.; writing—review and editing, S.N., L.G.-L., M.G.S.; supervision, L.G.-L., and M.G.S.; All authors have read and agreed to the published version of the manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Institutional Review Board Statement

The study was carried out following the deontological standards recognized by the Declaration of Helsinki (52nd General Assembly Edinburgh, Scotland, October 2000), the Standards of Good Clinical Practice, and approved by the Institutional Ethical Committee for social sciences of Jamia Millia Islamia, New Delhi, India. The aim of the research was explained to the participants, who gave their informed consent to the protocol and interviews. The data will be protected from uses not allowed by people outside the research, and the confidentiality of the same will be respected in accordance with Organic Law 15/1999 of December 13 on the Protection of Personal Data and Law 41/2002 of November 14, a basic law regulating patients’ autonomy and rights and obligations regarding information and clinical documentation.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available with the article and its supplementary material. The raw data that support he findings of the study are available from the corresponding author, upon request.

Acknowledgments

To the participants for their will to participate and sharing their experiences to help other people in distress.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Henrich, J.; Heine, S. J.; Norenzayan, A. Most people are not WEIRD. Nature 2010, 466, 29. [Google Scholar] [CrossRef]
  2. Muthukrishna, M.; Bell, A. V.; Henrich, J.; Curtin, C. M.; Gedranovich, A.; McInerney, J.; Thue, B. Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance. Psychological Science, 2020, 31, 678–701. [Google Scholar] [CrossRef]
  3. American Psychological Association. Guidelines for Psychological Practice for People with Low-Income and Economic Marginalization, 2019, Retrieved from www.apa.org/about/policy/guidelines-lowincome.pdf.
  4. American Psychological Association.Report of the APA task force on socioeconomic status. Washington, DC: American Psychological Association, 2007, Retrieved from. https://www.apa.org/pi/ses/resources/publications/task-force-2006.pdf.
  5. Gopalkrishnan, N. Cultural Diversity and Mental Health: Considerations for Policy and Practice. Frontiers in Public Health, 2018, 6. [CrossRef]
  6. Travasso, S. M.; Rajaraman, D.; Heymann, S. J. A qualitative study of factors affecting mental health amongst low-income working mothers in Bangalore, India. BMC Women’s Health, 2014, 14. [CrossRef]
  7. Jayaram, G.; Goud, R.; Srinivasan, K. Overcoming cultural barriers to deliver comprehensive rural community mental health care in Southern India. Asian Journal of Psychiatry, 2011, 4, 261–265. [Google Scholar] [CrossRef]
  8. Schüle, S. A.; Bolte, G. Interactive and Independent Associations between the Socioeconomic and Objective Built Environment on the Neighbourhood Level and Individual Health: A Systematic Review of Multilevel Studies. PLOS ONE, 2015, 10, e0123456. [Google Scholar] [CrossRef]
  9. Sahithya, B. R; Reddy, R. P. Burden of mental illness: a review in an Indian context. International Journal of Culture and Mental Health, 2018, 11, 553–563. [Google Scholar] [CrossRef]
  10. Sagar, R.; Dandona, R.; Gururaj, G.; Dhaliwal, R.S.; Singh, A.; Ferrari, A.; Dua, T.; Ganguli, A.; Varghese, M.; Chakma, J.K.; Kumar, G.A.; Shaji, K.S.; Ambekar, A.; Rangaswamy, T.; Vijayakumar, L.; Agarwal, V.; Krishnankutty, R.P.; Bhatia, R.; Charlson, F.; Chowdhary, N. The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990–2017. The Lancet Psychiatry, 2020, 7, 148–161. [Google Scholar] [CrossRef]
  11. Hankivsky, O.; Grace, D.; Hunting, G.; Giesbrecht, M.; Fridkin, A.; Rudrum, S.; Ferlatte, O; Clark, N. An intersectionality-based policy analysis framework: critical reflections on a methodology for advancing equity. International Journal for Equity in Health, 2014, 13, 13. [Google Scholar] [CrossRef]
  12. Trygg, N. F.; Månsdotter, A.; Gustafsson, P. E. Intersectional inequalities in mental health across multiple dimensions of inequality in the Swedish adult population. Social Science & Medicine, 2021, 283, 114184. [Google Scholar] [CrossRef]
  13. The Times of India. Post lockdown 15 million remained jobless till end 2020, 2020. https://timesofindia.indiatimes.com/india/post-lockdown-15-million-remained-jobless-till-end-2020-report/articleshow/82414788.cms. Assessed on 09th December 2023.
  14. Abeloff, M.D.; Armitage, J.O.; Lichter, A.S.; Niederhuber, J.E. Clinical Oncology (2nd ed.). Churchill Livingstone, 2000. 2000. [Google Scholar]
  15. Viertiö, S.; Kiviruusu, O.; Piirtola, M.; Kaprio, J.; Korhonen, T.; Marttunen, M.; Suvisaari, J. Factors contributing to psychological distress in the working population, with a special reference to gender difference. BMC Public Health, 2021, 21. [Google Scholar] [CrossRef] [PubMed]
  16. Drapeau, A.; Marchand, A.; Beaulieu-Prevost, D. Epidemiology of Psychological Distress. Mental Illnesses - Understanding, Prediction and Control. InTech; 2012. Available from: http://dx.doi.org/10.5772/30872. [CrossRef]
  17. American Psychological Association. Resolution on poverty and socioeconomic status,2000, Available at www.apa.org/about/policy/poverty-resolution.aspx.
  18. Pabayo, R.; Kawachi, I.; Gilman, S. E. Income inequality among American states and the incidence of major depression. Journal of Epidemiology and Community Health, 2014, 68, 110–115. [Google Scholar] [CrossRef] [PubMed]
  19. Gunnell, D. A Population Health Perspective on Suicide Research and Prevention. Crisis, 2015, 36, 155–160. [Google Scholar] [CrossRef] [PubMed]
  20. Burns, J. K.; Tomita, A.; Kapadia, A. S. Income inequality and schizophrenia: Increased schizophrenia incidence in countries with high levels of income inequality. International Journal of Social Psychiatry, 2013, 60, 185–196. [Google Scholar] [CrossRef]
  21. Lund, C. Poverty and mental health: Towards a research agenda for low and middle-income countries. Commentary on Tampubolon and Hanandita (2014). Social Science & Medicine, 2014, 111, 134–136. [Google Scholar] [CrossRef]
  22. Gupta, A.; Coffey, D. Caste, Religion, and Mental Health in India. Population Research and Policy Review, 2020, 39, 1119–1141. [Google Scholar] [CrossRef]
  23. Reimers, F. A.; Stabb, S. D. Class at the Intersection of Race and Gender. The Counseling Psychologist, 2015, 43, 794–821. [Google Scholar] [CrossRef]
  24. Diemer, M. A.; Mistry, R. S.; Wadsworth, M. E.; López, I.; Reimers, F. Best Practices in Conceptualizing and Measuring Social Class in Psychological Research. Analyses of Social Issues and Public Policy, 2012, 13, 77–113. [Google Scholar] [CrossRef]
  25. Cooper, S.; Lund, C.; and Kakuma, R. The measurement of poverty in psychiatric epidemiology in LMICs: critical review and recommendations. Social Psychiatry and Psychiatric Epidemiology, 2012, 47, 1499–1516. [Google Scholar] [CrossRef]
  26. Samuel, P.; Antonisamy, B.; Raghupathy, P.; Richard, J.; Fall, C. H. Socio-economic status and cardiovascular risk factors in rural and urban areas of Vellore, Tamilnadu, South India. International Journal of Epidemiology, 2012, 41, 1315–1327. [Google Scholar] [CrossRef] [PubMed]
  27. Roy, K.; Chaudhuri, A. Influence of socioeconomic status, wealth and financial empowerment on gender differences in health and healthcare utilization in later life: evidence from India. Social Science & Medicine, 2008, 66, 1951–1962. [Google Scholar] [CrossRef]
  28. Vaid, D. Caste in Contemporary India: Flexibility and Persistence. Annual Review of Sociology, 2014, 40, 391–410. [Google Scholar] [CrossRef]
  29. Deshpande, A. How India’s Caste Inequality Has Persisted—and Deepened in the Pandemic. Current History, 2021, 120, 127–132. [Google Scholar] [CrossRef]
  30. Pal, G. C. Caste–Gender Intersectionality and Atrocities in Haryana: Emerging Patterns and State Responses. Journal of Social Inclusion 2018. [Google Scholar] [CrossRef]
  31. Myer, L.; Stein, D. J.; Grimsrud, A.; Seedat, S.; Williams, D. R. Social determinants of psychological distress in a nationally-representative sample of South African adults. Social Science & Medicine, 2008, 66, 1828–1840. [Google Scholar] [CrossRef]
  32. Ramaiah, A. Dalits’ physical and mental health: Status, root causes and challenges. Tata Institute of Social Sciences. 2007.http://www.mfcindia.org/main/bgpapers/bgpapers2014/am/bgpap2014n.
  33. Kessler, R. C.; Barker, P. R.; Colpe, L. J.; Epstein, J. F.; Gfroerer, J. C.; Hiripi, E.; Howes, M. J.; Normand, S. L. T.; Manderscheid, R. W.; Walters, E. E.; Zaslavsky, A. M. Screening for Serious Mental Illness in the General Population. Archives of General Psychiatry 2003, 60, 186. [Google Scholar] [CrossRef]
  34. Saleem, S.M.; Jan, S.S. Modified Kuppuswamy socioeconomic scale updated for the year 2021. Indian J Forensic Community Med 2021, 8, 1–3. [Google Scholar] [CrossRef]
  35. Wani, R. Socioeconomic status scales-modified Kuppuswamy and Udai Pareekh’s scale updated for 2019. Journal of Family Medicine and Primary Care, 2019, 8, 1846. [Google Scholar] [CrossRef]
  36. Creswell, J. W. Educational Research: Planning, Conducting and Evaluating Quantitative and Qualitative Research (4th Ed.). Boston, MA, 2012. [Google Scholar]
  37. Smith, J. A.; Flowers, P.; Larkin, M. Interpretative phenomenological analysis: theory, research, practice; Sage: London, 2009. [Google Scholar]
  38. Creswell, J. W. Qualitative Inquiry and research design choosing among five approaches, 3rd Ed ed; Sage Publications: Thousand Oaks, CA, 2013. [Google Scholar]
  39. Alase, A. The Interpretative Phenomenological Analysis (IPA): A Guide to a Good Qualitative Research Approach. International Journal of Education and Literacy Studies, 2017, 5, 9. [Google Scholar] [CrossRef]
  40. Coffey, D.; Hathi, P.; Khalid, N.; Thorat, A. Measurement of population mental health: evidence from a mobile phone survey in India. Health Policy and Planning, 2021, 36, 606–619. [Google Scholar] [CrossRef] [PubMed]
  41. Abdi, F.; Rahnemaei, F. A.; Shojaei, P.; Afsahi, F.; Mahmoodi, Z. Social determinants of mental health of women living in slum: a systematic review. Obstetrics & Gynecology Science, 2021, 64, 143–155. [Google Scholar] [CrossRef]
  42. Shweder, R.A. Why do men barbecue?: Recipes for cultural psychology. Harvard University Press: Cambridge, MA, 2003. [Google Scholar]
  43. Maitra, S.; Brault, M. A.; Schensul, S. L.; Schensul, J. J.; Nastasi, B. K.; Verma, R. K.; Burleson, J. A. An Approach to Mental Health in Low- and Middle-Income Countries: A Case Example from Urban India. International Journal of Mental Health, 2015, 44, 215–230. [Google Scholar] [CrossRef] [PubMed]
  44. Mathias, K.; Pandey, A.; Armstrong, G.; Diksha, P.; Kermode, M. Outcomes of a brief mental health and resilience pilot intervention for young women in an urban slum in Dehradun, North India: a quasi-experimental study. International Journal of Mental Health Systems, 2018, 12. [Google Scholar] [CrossRef] [PubMed]
  45. Joshi, K.; Kumar, R.; Avasthi, A. Morbidity profile and its relationship with disability and psychological distress among elderly people in Northern India. International Journal of Epidemiology, 2003, 32, 978–987. [Google Scholar] [CrossRef]
  46. Srivastava, S. Purkayastha, N., Chaurasia, H. and Muhammad, T. Socioeconomic inequality in psychological distress among older adults in India: a decomposition analysis. BMC Psychiatry 2021, 21. [Google Scholar] [CrossRef] [PubMed]
  47. Naveen, K. S.; Goel, A.; Dwivedi, S.; Hassan, M. Adding life to years: Role of gender and social and family engagement in geriatric depression in rural areas of Northern India. Journal of Family Medicine and Primary Care, 2020, 9, 721. [Google Scholar] [CrossRef] [PubMed]
  48. Ghosh, A.; Mukhopadhyay, S. Influence of living arrangements on the psychological health of older women in slums. GeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry, 2022, 35, 81–94. [Google Scholar] [CrossRef]
  49. Khumalo, I. P.; Temane, Q. M.; Wissing, M. P. Socio-Demographic Variables, General Psychological Well-Being and the Mental Health Continuum in an African Context. Social Indicators Research, 2012, 105, 419–442. [Google Scholar] [CrossRef]
  50. Winefield, H. R.; Gill, T. K.; Taylor, A. W.; Pilkington, R. M. Psychological well-being and psychological distress: is it necessary to measure both? Psychology of Well-Being: Theory, Research and Practice, 2012, 2, p–3. [Google Scholar] [CrossRef]
  51. Oghagbon, E. K.; Giménez-Llort, L. Short height and poor education increase the risk of dementia in Nigerian type 2 diabetic women. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2019, 11, 493–499. [Google Scholar] [CrossRef]
  52. Palner, J.; Mittelmark, M. B. Differences between married and unmarried men and women in the relationship between perceived physical health and perceived mental health. Norsk Epidemiologi 2009, 12. [Google Scholar] [CrossRef]
  53. Parthasarathy, R.; TS, J.; K, T; Murthy, P. Mental health issues among health care workers during the COVID-19 pandemic – A study from India. Asian Journal of Psychiatry, 2021, 58, 102626. [Google Scholar] [CrossRef] [PubMed]
  54. Suryavanshi, N.; Kadam, A.; Dhumal, G.; Nimkar, S.; Mave, V.; Gupta, A.; Cox, S. R.; Gupte, N. Mental health and quality of life among healthcare professionals during the COVID-19 pandemic in India. Brain and Behavior, 2020, 10. [Google Scholar] [CrossRef] [PubMed]
  55. Bierman, A.; Fazio, E. M.; Milkie, M. A. A Multifaceted Approach to the Mental Health Advantage of the Married. Journal of Family Issues, 2006, 27, 554–582. [Google Scholar] [CrossRef]
  56. World Bank Poverty and Inequality Platform– processed by Our World in Data. Share of population living in extreme poverty – World Bank [dataset]. World Bank Poverty and Inequality Platform, 2022, World Bank Poverty and Inequality Platform 20220909_2017_01_02_PROD [original data]. Retrieved November 27, 2023 from https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty.
  57. Lund, C.; Breen, A.; Flisher, A. J.; Kakuma, R.; Corrigall, J.; Joska, J. A.; Swartz, L.; and Patel, V. Poverty and common mental disorders in low and middle income countries: A systematic review. Social Science & Medicine, 2010, 71, 517–528. [Google Scholar] [CrossRef]
  58. Wadsworth, P.; Kothari, C.; Lubwama, G.; Brown, C. L.; Frank Benton, J. Health and Health Care From the Perspective of Intimate Partner Violence Adult Female Victims in Shelters. Family & Community Health, 2018, 41, 123–133. [Google Scholar] [CrossRef]
  59. Greene, T.; Itzhaky, L.; Bronstein, I.; Solomon, Z. Psychopathology, risk, and resilience under exposure to continuous traumatic stress: A systematic review of studies among adults living in southern Israel. Traumatology 2018, 24, 89–103. [Google Scholar] [CrossRef]
  60. Subbaraman, R.; Nolan, L.; Shitole, T.; Sawant, K.; Shitole, S.; Sood, K.; Nanarkar, M.; Ghannam, J.; Betancourt, T. S.; Bloom, D. E.; Patil-Deshmukh, A. The psychological toll of slum living in Mumbai, India: A mixed methods study. Social Science & Medicine, 2014, 119, 155–169. [Google Scholar] [CrossRef]
  61. Potluri, S.; R. Patel, A. Using a Continuous Traumatic Stress Framework to Examine Ongoing Adversity Among Indian Women from Slums: A Mixed-Methods Exploration. Journal of Traumatic Stress, 2021, 34, 917–928. [Google Scholar] [CrossRef]
  62. Lakhan, R.; Sharma, M. Depression in Women Migrants Living in Slums in North India. Journal of Neurosciences in Rural Practice, 2021, 12, 223–223. [Google Scholar] [CrossRef] [PubMed]
  63. Chauhan, S. K.; Dhar, M. Prevalence and Predictors of Mental Health Disorder Among the Adolescent Living in the Slums of Lucknow, India: A Cross-Sectional Study. Community Mental Health Journal, 2019, 56, 383–392. [Google Scholar] [CrossRef] [PubMed]
  64. Anand, I.; Thampi, A. The Crisis of Extreme Inequality in India. The Indian journal of labour economics: the quarterly journal of the Indian Society of Labour Economics, 2021, 64, 663–683. [Google Scholar] [CrossRef]
  65. Ahammed, S. Caste-based Oppression, Trauma and Collective Victimhood in Erstwhile South India: The Collective Therapeutic Potential of Theyyam. Psychology and Developing Societies, 2019, 31, 88–105. [Google Scholar] [CrossRef]
  66. Johri, A.; Anand, P. V. Life Satisfaction and Well-Being at the Intersections of Caste and Gender in India. Psychological Studies, 2022, 67, 317–331. [Google Scholar] [CrossRef]
  67. Prost, A.; Lakshminarayana, R.; Nair, N.; Tripathy, P.; Copas, A.; Mahapatra, R.; Rath, S.; Gope, R. K.; Rath, S.; Bajpai, A.; Patel, V.; Costello, A. Predictors of maternal psychological distress in rural India: A cross-sectional community-based study. Journal of Affective Disorders, 2012, 138, 277–286. [Google Scholar] [CrossRef]
  68. Babu, B.V.; Kar, S.K. Domestic violence against women in eastern India: a population-based study on prevalence and related issues. BMC Public Health 2009, 9. [Google Scholar] [CrossRef]
  69. Dalal, K.; Lindqvist, K. A National Study of the Prevalence and Correlates of Domestic Violence Among Women in India. Asia Pacific Journal of Public Health, 2010, 24, 265–277. [Google Scholar] [CrossRef] [PubMed]
  70. Datta, A.; Satija, S. Women, development, caste, and violence in rural Bihar, India. Asian Journal of Women’s Studies, 2020, 26, 223–244. [Google Scholar] [CrossRef]
  71. Jungari, S.; Chinchore, S. Perception, Prevalence, and Determinants of Intimate Partner Violence During Pregnancy in Urban Slums of Pune, Maharashtra, India. Journal of Interpersonal Violence 2020, 37, NP239–NP263. [Google Scholar] [CrossRef]
  72. Klostermann, K. C.; Fals-Stewart, W. Intimate partner violence and alcohol use: Exploring the role of drinking in partner violence and its implications for intervention. Aggression and Violent Behavior, 2006, 11, 587–597. [Google Scholar] [CrossRef]
  73. Zaleski, M.; Pinsky, I.; Laranjeira, R.; Ramisetty-Mikler, S.; Caetano, R. Intimate Partner Violence and Contribution of Drinking and Sociodemographics. Journal of Interpersonal Violence, 2009, 25, 648–665. [Google Scholar] [CrossRef] [PubMed]
  74. Kalokhe, A. S.; Iyer, S.R; Kolhe , A.R.; Dhayarkar, S.; Paranjape, A.; del Rio, C.; Stephenson, R.; Sahay, S. Correlates of domestic violence experience among recently-married women residing in slums in Pune, India. PLOS ONE, 2018, 13, e0195152. [Google Scholar] [CrossRef] [PubMed]
  75. Begum, S.; Donta, B.; Nair, S.; Prakasam, C. P. Socio-demographic factors associated with domestic violence in urban slums, Mumbai, Maharashtra, India. The Indian Journal of Medical Research, 2015, 141, 783–788. [Google Scholar] [CrossRef] [PubMed]
  76. Kumar, S.; Jeyaseelan, L.; Suresh, S.; Ahuja, R. C. Domestic violence and its mental health correlates in Indian women. British Journal of Psychiatry 2005, 187, 62–67. [Google Scholar] [CrossRef]
  77. Dasgupta, A.; Battala, M.; Saggurti, N.; Nair, S.; Naik, D.; Silverman, J. G.; Balaiah, D.; Raj, A. Local social support mitigates depression among women contending with spousal violence and husband’s risky drinking in Mumbai slum communities. Journal of Affective Disorders, 2013, 145, 126–129. [Google Scholar] [CrossRef]
  78. Rosmarin, D. H.; Krumrei, E. J.; Andersson, G. Religion as a Predictor of Psychological Distress in Two Religious Communities. Cognitive Behaviour Therapy, 2009, 38, 54–64. [Google Scholar] [CrossRef]
  79. Behere, P.; Das, A.; Yadav, R.; Behere, A. Religion and mental health. Indian Journal of Psychiatry, 2013, 55, 187. [Google Scholar] [CrossRef]
  80. Weber, S. R.; Pargament, K. I.; Kunik, M. E.; Lomax, J. W.; Stanley, M. A. Psychological Distress Among Religious Nonbelievers: A Systematic Review. Journal of Religion and Health, 2012, 51, 72–86. [Google Scholar] [CrossRef]
  81. Martínez-Montilla, J. M.; Amador-Marín, B.; Guerra-Martín, M. D. Family coping strategies and impacts on family health: A literature review. Enfermería Global, 2017, 16, 592–604. [Google Scholar] [CrossRef]
  82. Bertolote, J. The roots of the concept of mental health. World Psychiatry, 2008, 7, 113–116. [Google Scholar] [CrossRef] [PubMed]
  83. Ghasemi, E.; Majdzadeh, R.; Rajabi, F.; Vedadhir, A.; Negarandeh, R.; Jamshidi, E.; Takian, A.; Faraji, Z. Applying Intersectionality in designing and implementing health interventions: a scoping review. BMC Public Health, 2021, 21, 1–13. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Psychological distress in Low Income and Economically Marginalized (LIEM) populations in India: Protective and Risk factors.

Dipti Singh

et al.

,

2023

Mental Health Issues in Madhya Pradesh: Insights from National Mental Health Survey of India 2016.

Arun Kokane

et al.

,

2019

The Psychological Impact of COVID-19 Pandemic among Communities Living in Dilla Town, Ethiopia, April 2020

Chalachew Kassaw

,

2020

Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated