Inner-city Youth’s mental health: the relations between resource loss, stressful life events, and psychological distress

ABSTRACT

This study explored the susceptibility of inner-city youth to stressful life events. It employed the Conservation of Resources (COR) theory to elucidate the impact of stressful events on their psychological distress. It tested the applicability of the Conservation of Resources Evaluation (COR-E) scale, previously used only on adults, on an inner-city youth sample comprising 309 Israeli adolescents from high-risk urban schools. Participants completed the Stressful Life Events Screening Questionnaire (SLESQ), an adapted version of the COR-E, and the Short Brief Symptom Inventory (BSI-18). Unique resource categories emerged, grounded in the youths’ immediate environment. Positive correlations were established between resource loss and stressful life events, and heightened resource loss was associated with increased psychological distress. The study underscores the utility of the COR framework in comprehending the intricate relationship between psychosocial resource loss and the psychological well-being of inner-city youth.

KEYWORDS:

• Inner city youth
• conservation of resources theory
• mental health
• psychological distress
• stressful life events

Introduction

Urban areas, particularly inner cities, are disproportionately affected by poverty, with a significant proportion of individuals experiencing economic hardship residing in these communities. Statistics reveal that 84% of impoverished individuals in the United States are living in urban environments (De Navas-Walt & Proctor, 2016). This socioeconomic disparity poses specific challenges, particularly for children. Studies indicate that children growing up in inner-city environments are at elevated risk of unemployment, racial and ethnic segregation, and exposure to stressful life events such as neighbourhood violence or family illness (e.g. Eisman et al., 2018; Evans & Kim, 2012; Zimmerman et al., 2022). Additionally, poverty and exposure to stressful life events have been associated with adverse effects on brain development in youth (Luby et al., 2013). However, the relationship between exposure to stressful life events and psychological difficulties is seldom straightforward and often complex. Multiple factors, including individual experiences of stress and personal, familial, and community resilience, may influence the psychological outcomes of growing up in these environments (Hsieh et al., 2022; Sanchez et al., 2013; Slone & Mayer, 2015).

Various life events measurement scales have been used to chart the life circumstances of children and youth in inner cities (Coddington, 1990; Gonzales & Samaniego, 1999; Richters & Saltzman, 1990). These instruments measure levels of exposure to certain life events, such as parental separation, violence, or personal illness, and subjectively evaluate the impact of the exposure. Studies examining the influence of stressful life experiences on youth have found a range of corresponding physical and psychological symptoms (Luby et al., 2013). Youth exposed to stressful life experiences have been identified as ‘adolescents at risk’ for developing academic, emotional, social, functional, and behavioural difficulties, post-traumatic symptoms, and psychological distress (Bolton et al., 2004; Masten, 2013, 2014). However, the symptomatic picture provides only partial insight into the processes underlying the impact of the stressful inner-city environment. This study aimed to deepen knowledge of the impact of personal, societal, communal, and cultural adversities by drawing on the Conservation of Resources (COR) theory and measuring resource loss (Hobfoll, 2011, 2012).

Conservation of resources theory

COR theory (Hobfoll, 2011, 2012) is a central framework used to explain the direct impact of stressful situations and a well-established theory in the field. A central proposition of COR is that humans are driven by the need to acquire, preserve, protect, and nurture resources. A resource is defined as anything that has value for someone within the context of a particular culture (Hobfoll, 2011, 2012). In early work on adults, Hobfoll and Lilly (1993, p. 129) identified four categories of resources: (1) objects and material resources (e.g. transportation and shelter); (2) conditions (e.g. work security, seniority, and family stability); (3) personal characteristics (e.g. social ability, self-esteem) which may provide access to other valuable resources (e.g. energies or work skills which provide access to financial resources); (4) energies (e.g. money and insurance) which propagate access to objects, conditions or personal resources. According to COR, psychological stress arises when individuals perceive a threat of resource loss or when they actually lose a resource, such as money, hope, or health (Hobfoll, 2011, 2012).

Numerous studies have supported the COR theory and have found positive correlations between the degree of perceived resource loss and negative mental health consequences (e.g. Bardoel & Drago, 2021; Hollifield et al., 2016; Laeeque et al., 2022; Li & Zhu, 2022; Saltzman et al., 2022).

Resource loss among inner-city youth

The fundamental tenets of COR theory have rarely been examined among adolescents. In two exceptions, research focusing on the long-term effects of Hurricane Katrina on adolescents found the loss of psychological resources was a significant determinant of psychological distress (Blaze & Shwalb, 2009; Robertson et al., 2009). Correlations have also been found between possessing resources and productive coping skills and, conversely, between a lack of resources and unproductive coping skills among secondary school students (McKenzie et al., 2004). A recent study found the COR theory could explain why sibling abuse affects some adolescents more severely than others (Laeeque et al., 2022).

Given the relevance of the COR theory to understanding sources of stress, the paucity of research on resource loss and mental health among youth, particularly at-risk youth, is unfortunate. Adolescence is characterized by rapid developmental changes involving negotiation of new emotional and social challenges, formation of identity, learning of new skills, and development of autonomy (Dahl et al., 2018). These experiences could have a compound effect on inner-city youth who may endure chronic resource loss while negotiating the challenges of adolescence. The present study contributed to the literature by exploring the psychosocial resource losses of at-risk inner-city youth using an adapted version of the Resource Loss Evaluation scale and examining the impact of these losses on their mental health.

The study was conducted in Israel with an inner-city youth sample of at-risk students in a vocational school system categorized as a ‘second chance’ programme. Second chance schools are designed to provide an alternative educational structure for youth who drop out of the public school system for a variety of reasons, including familial and educational difficulties (Hen-Gal & Sapir, 2013). A previous study found these youth are often exposed to stressful acute and chronic life events, such as poverty, familial issues, parental neglect, and physical or emotional trauma (Hen-Gal & Sapir, 2013). Possibly as a result of these life events and adversities, many have behavioural and educational difficulties, which contribute to their dropping out of the regular school system (Carpenter-Aeby & Kurtz, 2000; Hen-Gal & Sapir, 2013). A constellation of difficult background circumstances, school drop-out, and limited resources positions these adolescents at high risk for developing psychopathology and severe behavioural difficulties (Aron, 2006; Grunbaum et al., 2000; Tobin & Sprague, 2000).

This exploratory study adds to the literature by viewing at-risk youth’s adversity and distress through a COR theoretical lens. More specifically, it addressed relations between resource loss and inner-city at-risk youth’s adverse life events and levels of psychological distress, testing the applicability of a new scale, the Conservation of Resources Evaluation (COR-E), to assess resource loss. The study formulated one exploratory question and three research hypotheses. The exploratory question asked if resource loss categories among youth resemble those among adults. The first hypothesis was that inner-city youth will have higher levels of psychological distress than the general youth population. The second hypothesis argued resource loss and stressful life events will be positively related to psychological distress but resource loss will have a larger effect than the stressful life events effect. The third hypothesis was that the overall resource loss will be homogeneous across resource-loss dimensions, and higher resource loss will be associated with more negative psychological outcomes and vice versa.

Methods

Participants

Research participants were 309 Israeli adolescents aged 15.0 to 18.0 (M = 16.61, SD = .77). Participants were students in grades 10 through 12. Of the full sample, 214 participants identified as men, 90 identified as women, and 5 preferred not to disclose their gender. They were all students in two second chance schools in an inner-city location in Israel. One hundred and three identified as secular, 95 as traditional, and 35 as religious; 76 chose not to classify their degree of religiosity. Students were requested to identify their socioeconomic status on a scale from 1 (low) to 3 (high). The mean socioeconomic status was M = 1.38, SD = .78.

Instruments

Demographic questionnaire

The demographic questionnaire included age, gender, socioeconomic status, and degree of religiosity.

Stressful life events screening questionnaire (SLESQ)

The SLESQ (Coddington, 1990) assesses participants’ exposure to negative life events and self-reported impacts of exposure to these events. It includes a range of stressful life events (e.g. serious illness, divorce). Participants in this study were asked to indicate which stressful life events had occurred over the past year by marking exposure to items of specific events, and in the case of exposure, to rate the impact of the event on a Likert scale ranging from 1 (did not affect me) to 5 (exposure affected me greatly). Impact of exposure was calculated as the mean score across impact ratings. The questionnaire was distributed in Hebrew; previous studies found the Hebrew version has high validity (Lavi & Slone, 2011; Meir et al., 2012). Internal consistency is not applicable for this instrument as separate events are not contingent on one another.

Conservation of resources evaluation (COR-E)

To measure resource loss, we used a modified version of the COR-E questionnaire (Hobfoll & Lilly, 1993). Participants were requested to estimate the extent of resources lost over the past year from a list (e.g. money, sleep time, clothes, a close relationship) on a scale from 1 (did not lose any [of this resource]) to 3 (lost a lot [of this resource]). The original resource questionnaire includes 74 items. To customize the questionnaire for an adolescent population, items irrelevant to adolescents were removed from the list (e.g. loss of pension, loss of marriage), cutting the final questionnaire down to 40 items. Customizing the list of resources for a specific population is a well-known practice in the resource loss literature (Bansal et al., 2000; Jackson et al., 2001), and no items were added in order to adhere to the methodology used in similar studies. The COR-E has been found to be reliable; Cronbach’s alpha in the present study was α = 0.86.

Short brief symptom inventory (BSI-18)

Psychological distress and psychiatric symptomatology were measured using the BSI-18 (Derogatis, 2001). The BSI-18 is used to assess the level of psychological distress and psychological symptoms in both clinical and non-clinical populations. The questionnaire includes 18 symptom items that participants rate for severity of experience on a Likert scale from 0 (not at all) to 4 (very much). The questionnaire yields a general psychological distress score, called the general severity index (GSI), derived from the mean of ratings across all items, and three subscale scores: somatization, depression, and anxiety. The subscales only reflect symptoms of internalization. Therefore, we added the 6-item hostility subscale from the original 54-item BSI questionnaire. The hostility subscale items reflect externalization symptoms and can be used as a stand-alone measure within the framework of the complete BSI (Derogatis & Spencer, 1982). The BSI-18 questionnaire has been found to have high internal reliability for the general distress index (α = .89) and for the subscales (Cronbach’s alpha ranges from α = .71 to α = .88) (Andreu et. al., 2008). The questionnaire has shown good validity, with a high correlation (equal or higher than 0.3) between the BSI scales and the Minnesota Multiphasic Personality Inventory (MMPI) scales (Boulet & Boss, 1991). The Hebrew version, which has been used in previous studies, was used here (Lavi & Slone, 2011; Slone & Shechner, 2009). The internal reliability of the BSI was high for the GSI score, α = .91. The reliability of the four sub-scales was also high: α = .85 for somatization, α = .78 for depression, α = .76 for anxiety, and α = .85 for hostility.

Procedure

The study was approved by the Tel Aviv University Ethics Committee, the Ministry of Welfare, which is the government office responsible for the vocational high schools, and the principals of the two schools sampled. Parents and students provided written informed consent to participate in the study. The research team included undergraduate psychology students who had participated in a special training seminar. Questionnaires were completed by students during regular classes. Students were informed that participation was voluntary, and they could withdraw from the study at any time with no consequences. Statistical analyses were conducted using the SPSS 26 package.

Modeling strategy

The exploratory question was tested using exploratory factorial analysis. To test the first research hypothesis, we compared the mean scores for distress and anxiety to a known national level by using a one-sample t-test against expected value. To test the second research hypothesis, we correlated the psychological distress outcomes with resource loss and stressful life events, using a linear regression model to compare the contribution of each in explaining the variance of the targeted outcomes. To test the third hypothesis, we first defined groups of high versus low resource loss, employing latent class analysis (LCA; Collins & Lanza, 2010) and the Lo-Mendell-Rubin test for N + 1 versus N latent classes (Lo et al., 2001) to determine the more representative latent class. We used a multivariate analysis of variance (MANOVA) model to confirm that the latent classes were associated with different distress and stress outcome levels.

Results

Validity of COR-E for a youth sample

To test the exploratory question asking if findings for COR-E in a youth sample were similar to findings among adults, we used exploratory factor analysis with a varimax rotation solution. Based on the eigenvalue criterion (eigenvalue >1.00; Beavers et al., 2013), five factors emerged: personal resources (e.g. self-esteem), financial resources (e.g. financial assistance), familial resources (e.g. stability in family, positive relationships with parents), social resources (e.g. close relationships with friends, support from classmates), and academic resources (e.g. time to study, feeling of progress in studies). The five factorial model accounted for 43% of the variances in the scale. Item loadings, reliability, and descriptive characteristics are presented in Table 1. Reliability for each factor was sufficient. Face and content validity of the factors were good; all items included in each group represented the sample and matched the content of the category/factor. The definition of each factor was based on the loadings of groups of items in each specific factor. Cronbach’s alpha, mean and SD, reliability, and factor loadings for each item are presented in Table 1. Overall, the factor analysis suggested different clusters of resources for adolescents than for adults. In the study sample, resources were distributed as per areas of life and relationships; for example, the resource ‘home’ under the ‘objects cluster’ for adults, was a ‘family resource’ for youth. The COR-E had good internal consistency for the youth sample.

Table 1. Factor analysis of resource loss items.

The items	Factor 1 Personal Resources loss	Factor 2 Financial Resources Loss	Factor 3 Family Resources Loss	Factor 4 Social Resources loss	Factor 5 Educational Resources Loss
Reliability by Alpha’s Cronbach	.87	.87	.78	.73	.63
Mean	1.34	1.42	1.26	1.39	1.57
Standard Deviation	0.33	0.52	0.35	0.38	0.40
1. Good feeling about myself	0.65	0.26	0.15	0.07	0.14
2. Feeling that I am successful	0.64	0.06	0.21	0.05	0.08
3. Ability to control my life	0.63	0.02	0.34	0.13	0.03
4. Hope	0.61	0.23	0.07	0.24	0.05
5. Feeling I control my life	0.58	−0.06	0.43	0.13	0.00
6. Feeling it will be good	0.57	0.36	0.19	0.10	0.07
7. Feeling I’m achieving my goals	0.57	0.13	0.08	0.18	0.25
8. Feeling my life has a purpose	0.55	0.19	0.26	−0.12	0.21
9. Feeling others think I’m successful	0.55	0.13	0.00	−0.02	0.07
10. Will to do things	0.49	0.19	0.15	0.20	0.11
11. Self-pride	0.49	0.03	0.05	0.23	0.12
12. The feeling that success is up to me	0.48	−0.03	0.35	0.10	0.12
13. Role of a leader	0.46	0.03	0.05	0.21	0.09
14. Loyalty from friends	0.45	0.22	0.04	0.17	0.10
15. Sense of humour	0.42	0.08	−0.05	0.19	0.03
16. Feeling that life is calm	0.36	0.19	0.22	0.31	0.20
17. Financial Help	0.22	0.82	0.12	0.17	0.08
18. Money to spend	0.14	0.77	0.18	0.11	0.04
19. Money for emergencies	0.21	0.73	0.22	0.00	0.10
20. Good financial state	0.31	0.71	0.14	0.12	0.08
21. Money for travels	0.09	0.67	0.21	0.18	0.09
22. Good relationship with my parents	0.24	0.09	0.76	0.04	0.03
23. Family stability	0.19	0.23	0.70	0.04	0.02
24. Close relationship with someone from the family	0.18	0.17	0.69	0.07	0.02
25. House that fits what I need	−0.03	0.31	0.54	0.26	0.12
26. Self-discipline	0.26	0.00	0.49	0.20	0.21
27. Food	0.06	0.23	0.49	0.16	0.03
28. Help with household tasks	0.22	0.22	0.33	−0.06	0.31
29. Help from my classmates	0.13	0.12	−0.01	0.62	0.23
30. The ability to be in contact with others	0.36	0.18	−0.02	0.60	0.11
31. Close connection to friends	0.28	0.18	0.09	0.59	0.03
32. Time off with friends	0.06	0.04	0.23	0.56	0.21
33. Time to spend with the ones I love	0.16	0.16	0.32	0.55	0.15
34. The feeling that others care for me	0.33	−0.03	0.15	0.53	−0.11
35. Advancement in school	0.26	0.05	−0.06	−0.05	0.62
36. What I need to study	0.14	0.04	0.04	0.26	0.60
37. Understanding from my teachers	0.19	−0.11	0.05	0.07	0.60
38. Time for education	0.02	0.04	0.00	0.08	0.58
39. Help in education	0.14	0.16	0.18	0.24	0.56
40. Involvement in religious activities	−0.01	0.17	0.14	0.04	0.39

Relations between psychological symptom levels, stressful life events, and resource loss

To test the first research hypothesis, namely that inner-city youth have higher levels of distress than others, we compared the BSI scores for our sample to the BSI norms for Israeli adolescents aged 14 to 18 (Al-Krenawi et al., 2009). Four one-sample t-tests revealed that the participants’ level of anxiety was significantly higher than the norm (M = 1.96, SD = .75 vs. M = 1.13, SD = .77), t(306) = 19.39, p < .001. Their levels of depression were significantly higher (M = 1.79, SD = .77 vs. M =.96, SD = .76), t(300) = 18.73, p < .001, as were their somatization symptoms (M = 1.64, SD = .81 vs. M = .81, SD = .71), t(299) = 17.80, p < .001, and their levels of hostility (M = 2.29, SD = 1.02 vs. M = 1.11, SD = .93), t(300) = 20.04, p < .001. These findings support the premise that the sample represented a group of high-risk youth.

Relations between resource loss, adverse life events, and psychological distress

The second hypothesis, namely that resource loss and stressful life events would be positively related to psychological distress, was supported by the results of a series of linear regressions (see Table 2). The portion of the explained variance by resource loss in the GSI and depression was greater than that of the stressful life events but they were similar in explaining anxiety, somatization, and hostility.

Table 2. Association between resource loss and stressful life events to psychological distress subscales, standardized regression coefficients.

	General psychological distress (GSI)	Depression	Anxiety	Somatization	Hostility
General level of resource loss	.436**	.514**	.244**	.264**	.252**
Stressful life events	.259**	.205**	.205**	.261**	.190**
R^2	.330**	.381**	.128**	.182**	.131**

df = 2,303, **p < .01, *p < .05.

Differences in psychological symptoms for low and high resource-loss groups

The third hypothesis was that the overall resource loss would be homogeneous across resource-loss dimensions, and higher resource loss would be associated with more negative psychological outcomes and vice versa. To test this hypothesis, we constructed a categorical index for high and low resource-loss groups as a factor of the differences in psychological distress outcomes. Latent class analysis (Wang & Wang, 2012) generated two independent resource-loss clusters across the five resource-loss dimensions. This method is a common tool for detecting groups in a sample according to a series of variables (the resource clusters). In this case, it allowed us to identify latent groups experiencing particularly high levels of resource loss. The decision on the optimal number of groups is based on the entropy index measuring improvement resulting from the distribution to more groups (Lo-Mendell-Rubin Likelihood ratio) and on the Bayesian information criterion (BIC) and Akaike ‘s information criterion (AIC) indices (these are reduced if a larger number of groups contributes to the model’s improvement) (Wang & Wang, 2012).

Table 3 shows the two groups representing two levels of resource loss. One group represents a low resource-loss level (n = 255), with participants in this group receiving a score of 0 in general resource-loss level. The second group represents a high resource-loss level (n = 54), with participants in this group receiving a score of 1 in general resource-loss level. Figure 1 shows the differences between levels of loss of resources in the two latent groups.

Figure 1. Differences between levels of resources loss in the two latent groups (high loss versus low loss).

Table 3. Comparison between groupings in an increasing number of groups.

	One group	Two groups	Three groups	Four groups
AIC	1480.97	1013.41	888.51	845.86
BIC	1518.27	1073.09	970.58	950.30
Log-likelihood	−730.49	−490.70	−422.26	−394.93
Entropy		.93	.90	.84
Number of students	308	255	233	190
		53	61	51
			14	14
				53
Vuong-Lo-Mendell		.001	.07	.35
Lo-Mendell-Rubin		.001	.07	.35
Test for N-1 versus N clusters		<.001	<.001	<.001

Multivariance tests were conducted to examine whether high versus low resource-loss groups showed differences in levels of psychological distress. We used a MANOVA test to assess the effect of resource loss on the indicators of psychological stress mentioned above: GSI, depression, anxiety, somatization, and hostility. The hypothesis was supported: an overall difference emerged between loss levels beyond the specific indicator (F = 14.44, p < .001, Wilk’s lambda = .81), yet all indicators showed a significant difference (p < .001) independently (see Table 4). The analysis of effect size (${\eta }_{pa\mathrm{rtial}}^2$) found a large effect for depression and a medium effect for somatization, anxiety, and hostility. These results support the validity of the COR-E scale for distinguishing between the two latent groups in the sample of high-risk youth and to identify an even more vulnerable sub-group of participants who had experienced even greater losses.

Table 4. Multivariate analysis of variance of psychological stress indicators by levels of loss.

	Low (n = 254)		High (N = 53)
	Mean	SD	Mean	SD	F(1,305)	ηp2
Depression	1.64	0.66	2.51	0.83	68.77***	.18
Anxiety	2.32	0.86	2.87	1.00	17.33***	.05
Somatization	1.40	0.74	2.12	1.08	35.38***	.10
Hostility	2.19	0.97	2.77	1.11	14.64***	.05

MANOVA results: F(5,301) = 14.44, p < .001, eta = .19, Wilk’s Lambda = .81, ***p < .001.

Discussion

The principles of the COR theory were at the centre of this study. The theory emphasizes that a basic component of responses to stress is the human need to preserve existing resources in the environment and culture (Hobfoll & Lilly, 1993). The theory has mostly been tested among adults our results suggest resource loss is an important factor in a youth population as well. Previous work has explored stressful life events experienced by adolescents using socio-economic status and family indices, but studies have rarely focused on psychosocial outcomes. A few studies conducted among youth have found specific resource losses are positively associated with psychological distress or mental well-being; however, these studies did not comprehensively examine the overall loss of resources, focusing instead on one or two specific resources (Blaze & Shwalb, 2009; Robertson et al., 2009). Furthermore, previous research has largely focused on the repercussions of acute stressful events, such as natural disasters or war, not on the more routine and prevalent stressors encountered in the daily lives of youth (e.g. Hackbarth et al., 2012; Hobfoll et al., 2012; Slobodin et al., 2011).

Prior studies on adults identified four resource types: energies, objects, conditions, and personal characteristics (Hobfoll & Lilly, 1993). This exploratory investigation identified novel resource categories shaped by adolescents’ perceptions of resources provided by significant individuals in their lives, such as parents, teachers, and friends. For example, positive relationships with parents, access to food, and assistance with household tasks formed a category encapsulating family resources. This emphasizes the vital role of the immediate environment, family, and community in adolescents’ lives, highlighting their profound impact on well-being. Consequently, the findings not only expand our understanding of adolescent stress but also underscore that it is intricately interwoven with and influenced by the relational context in which it unfolds.

The first research hypothesis was substantiated: the youth sample exhibited elevated psychological distress across all measures compared to the general population. This aligns with findings of previous studies on the exposure of inner-city youth to stressful life events (e.g. Eisman et al., 2018; Evans & Kim, 2012; Zimmerman et al., 2022). These consistent findings suggest the need for dedicated attention and support to ensure the development and well-being of inner-city youth.

The second research hypothesis stated that resource loss and stressful life events would be positively related to psychological distress but resource loss would have larger effect size than the stressful life events. This hypothesis was partially confirmed. Stressful life events and resource loss were both positively associated with the psychological distress outcome. The portion of the explained variance by resource loss in depression and the GSI (which was probably impacted by the depression level) was greater than that of the stressful life events, but these effects were similar in explaining anxiety, somatization, and hostility. This suggests that resource loss may be a better predictor of depression levels than mere exposure to stressful life events. This supports the convergent validity of the COR-E scale as a valid tool to assess stress and, at the same time, the discriminant validity of the measurement of exposure to stressful life events. The finding that anxiety, somatization, and hostility variances were similarly explained by stressful life events and resource loss may imply that depression is impacted by resource loss more than other types of distress.

The third hypothesis was also confirmed, with findings of homogeneity between types of resource loss and cross-sectional differences in the scores on psychological measures for low and high resource-loss groups, such that the high resource-loss group experienced more psychological distress than the low resource-loss group. The findings align with the predictions of the COR theory in two significant ways. First, in accordance with the COR theory, we found a correlation between high resource loss and elevated psychological distress (Hobfoll, 2011, 2012). Second, substantiating the COR theory of resource repositories or ‘resource caravans’ (Hobfoll, 2011, 2012), we found an association between the levels of loss across various resource domains. Adolescents facing resource loss in one area tended to experience losses in other areas as well. This underscores the heterogeneous and varying levels of risk even within high-risk populations.

Research limitations

A limitation of the study was the use of self-report questionnaires, as these tools provide a one-dimensional and subjective perspective. Previous research has demonstrated gaps between informants when measuring adolescents’ mental health outcomes (Meir et al., 2012). External measures such as student scores or overall performance in the classroom may have provided additional information about their general functioning, but studies have found that external reports do not necessarily reflect personal feelings about psychological distress among adolescents (Stevens & Volleberbergh, 2008).

Another limitation was the study sample, which may not fully represent the diversity of Israeli inner-city experiences, as it originated from only two schools. A more nuanced understanding of resource factors among adolescents could be attained by including youth not enrolled in second-chance schools. Moreover, the study’s conceptual framework, emphasizing the loss of resources among youth, might have inadvertently disregarded situations where individual participants lacked access to a specific resource from the outset. The COR-E scale does not differentiate between instances when individuals have lost a resource and instances when they never had the resource in the first place. Finally, the dependent variables only included measures of psychological distress. Positive feelings or aspects of mental well-being were not examined; including them would have broadened the findings.

Research implications

This research represents a pioneer study of resource loss among inner-city youth, turning the spotlight on a population who is often consigned to the margins of society. The findings suggest the need to consider how support for these youth can be enhanced to help them cope with loss and improve their resources. Future research should continue to examine resources in this population with a view to determining whether interventions can interrupt the spiral of resource loss. For example, the value of interventions enhancing social-emotional skills or supporting occupational and work-related aspects could be investigated, including whether these could contribute to resource gain, rather than merely prevent resource loss. In addition, longitudinal studies could explore the dynamics of resource loss and gain as these play out in inner-city environments.

Mental health professionals play a vital role in monitoring and diagnosing interpersonal differences among at-risk adolescents. The ability to identify youth who have experienced extensive losses would enable tailored support, and this exploratory research suggests the COR-E scale may be a valuable screening tool. Beyond simply identifying exposure to stressful life events, it seems imperative for mental health professionals to recognize the subjective impact of these events. Understanding how youth perceive and process these events in nuanced ways may improve the effectiveness of interventions and support strategies. The COR-E scale may further assist in pinpointing areas where youth have experienced more losses, facilitating the customization of support in treatment programmes.

Overall, this study underscores the importance of continuing to explore how societal factors influence the mental health of inner-city youth. The results should encourage decision-makers to direct support and funding towards programmes aimed at preventing further resource loss among inner-city youth and promoting their overall well-being, development, and thriving.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the The French Friends of Tel Aviv University .