Background
Increasing numbers of people are leaving their country of birth because of conflict, poverty, unemployment, or in search of higher quality of life. The
International Organisation for Migration (IOM) estimated that as of June 2019, the number of international migrants was almost 272 million globally [
1]. Although migration is not a recent phenomenon, research into its impact on wellbeing and quality of life remains relatively sparse [
2,
3]. Whilst many migrants have experienced multiple forms of trauma and life-threatening situations prior to and during the process of migration [
4], more recently research attention has also recognized that the living conditions and post-migration stressors experienced in the settlement environment can exert an important influence on their mental health and wellbeing [
5,
6]. Post-resettlement conditions have been suggested to be at least of equal importance for the mental health and wellbeing of migrants as pre-migratory conditions [
7‐
9], including both forcibly displaced (i.e., asylum seekers and refugees, [
10‐
12]), and non-forcibly displaced migrants (i.e., economic/labor migrants, [
13]).
In particular, the United Nations High Commissioner for Refugees (UNHCR) underlines that some migrant subgroups are more disenfranchised than others, i.e. those with intersecting identities that may infer additional disadvantage including women/girls, children, persons with disabilities, sexual minorities and elderly men [
14,
15]. The intersecting challenges, such as challenges related to gender, immigrant status and forced migration, might add up or even mutually reinforce each other [
16], which can present multiple challenges to their overall integration, wellbeing and ability to live a full life post-resettlement [
16‐
19].
To date, few assessment instruments have been developed to quantitatively measure the mental health status of migrant populations specifically. The majority of tools that have been developed have focused on (i) specific subgroups of migrants, most commonly asylum seekers and refugees, and (ii) measuring pre-migration sources of trauma and distress (i.e. The Harvard Trauma Questionnaire, [
20]), or post-migration stressors and risk factors for mental ill-health (i.e. the Post-Migration Living Difficulties Scale; [
21]; the Refugee Post-Migration Stress Scale, [
22]), rather than positive mental health and/or wellbeing outcomes. There have been a number of calls in the literature to move away from the focus on psychopathology, and instead move towards broader outcomes relevant to psychosocial functioning in migrant groups [
2,
23,
24]. It is argued that the dominant focus on trauma and distress overlooks other aspects of migrants’ mental health and wellbeing, for example relationships, sense of meaning [
25] and sense of belonging [
26]. This aligns with the recognition that mental health and wellbeing are not simply the absence of disease but instead encompass a wider understanding of what brings vitality into a person’s lived experiences, and that high levels of wellbeing and an understanding of positive predictors of mental health are necessary as well [
27,
28]. As such, assessing levels of positive mental health and/or levels of wellbeing and identifying factors associated with higher levels of mental health and wellbeing has been highlighted [
28,
29].
A recently conducted study used a participatory research approach to develop a wellbeing scale for a sample of newly resettled refugees from Myanmar and Bhutan in the USA [
30]. The scale was developed in the context of an agricultural program aimed at strengthening health and improving wellbeing. The initial scale was composed of three subscales namely, (i) somatic experience, (ii) occupational balance, and (iii) social inclusion/self-identification. The authors acknowledged that “future iterations of survey development could include a factor analysis to measure the fit of a latent variable of wellbeing to the selected survey items, or correlation with similar, existing measures” [[
30], p.27]. It is likely that assessment instruments of this kind could be beneficial for guiding policy, monitoring wellbeing, and identifying areas that require further support and attention for specific migrant groups post-resettlement.
The capability approach
Sen’s Capability Approach (CA, [
31]) is widely regarded to be of substantive importance for the conceptualization of multidimensional wellbeing [
32,
33]. The CA holds that the wellbeing of a person ought to be assessed in the space of capabilities; the abilities to achieve the ‘beings and doings’ that they have reason to value in life [
31]. From a CA perspective, human wellbeing depends on what resources enable people to do and to be. The ability to convert resources (e.g., social networks or education) into what people consider to be a
good life varies and can include both health and non-health related variables like empowerment, relationships, participation, housing, and legal status [
34]. As such, the CA not only assesses a person’s current circumstances, it also includes a focus on outcomes, agency and the individual’s substantive opportunities to achieve wellbeing [
35].
The relevance and utility of applying the CA in the context of migration has been highlighted in a recent theoretical commentary by White & van der Boor [
24]. The authors proposed the CA as a helpful framework to elucidate a focus on what living well means to migrant groups, understand what resources are available to these groups, and how these resources might interact with the persons’ capabilities and freedoms to engage in valuable functionings [
24]. Factors operating at different levels of an individual’s social environment including their microsystem (i.e. factors that directly affect the individual), mesosystem (i.e. factors that impact on the social experience of the individual), exosystem (i.e. factors that are experienced by those in the person’s social networks) and the macrosystem (i.e. factors that operate at an institutional level) were highlighted as important when formulating an understanding of migrants’ experiences [
24]. The authors concluded that individual choices, resources and entitlements will be highly influenced by people’s migration status [
24,
36].
Significant attempts have been made to create evaluative tools and measures that are based on the CA. For example, the ‘
Human Development Index’ published by the United Nations Development Program [
37] is grounded in the understanding of development as a process of expanding individuals’ choices and opportunities. More recently, the
Oxford Poverty and Human Development Initiative developed a specific measure of poverty [
38], and the Organisation for Economic Co-operation and Development’s (OECD)
Better Life Index which was launched in 2011 and aimed to measure the national wellbeing of OECD member countries [
39]. However, there has been concern about the lack of available data relating to people’s actual capabilities, rather than the outcome of these capabilities (i.e., their functioning) ([
40], for a review see Robeyns, [
41]). Anand et al. [
40] developed a list of over sixty capability indicators which could be used to generate information about an individual’s capabilities. This capability list was reduced and refined by Lorgelly et al. [
42] into an 18-item capability wellbeing index (OCAP-18) and was validated for use in public health evaluations in Glasgow, UK with members of the public. Subsequently, Simon et al. [
43] adapted the OCAP-18 to create the OxCAP-MH; a 16-item capability informed wellbeing measure for mental health research. The OxCAP-MH allows for the identification of capability domains most affected by mental illness and was validated on a sample of adults who had been involuntarily treated in hospital. To date, however, the CA has not been used to operationalize a measure of wellbeing for migrant populations.
A key objective of this paper is to describe the development of the ‘Good Life in the Community Scale’ (GLiCS) which was developed using the CA [
31] as a guiding framework and coproduced with members of migrant populations in the United Kingdom (UK). To develop the items on the GLiCS, qualitative data collected in a previous study that explored what constitutes a ‘good life’ for female refugees in the UK from the perspective of the CA was used [
44]. Specifically, the wording used by the participants to describe each domain relevant to achieving a ‘good life’ was extracted from the transcripts and used to create an initial draft of 88 individual items. In line with previous research that has highlighted the importance of liaising with experts by experience in the development of assessment instruments [
45,
46], the current paper describes a multi-phase approach to the development of the GLiCS involving women with a lived experience of migration and/or supporting migrants. In addition, this paper also provides a preliminary assessment of the psychometric properties of the GLiCs.
Results
Factor structure
Parallel analysis of the 42 item GLiCS (v0.3) suggested there were up to six underlying factors, this was used as an upper limit to the number of factors when exploring the structure in the EFA. The Kaiser–Meyer–Olkin measure suggested the sample was adequate (KMO = 0.50) and Bartlett’s test of sphericity demonstrated that correlations between the items were large enough for EFA (χ2 (861) = 6887.394, p < 0.001).
Factor one had an Eigenvalue of 5.82 (variance explained = 14%), factor two Eigenvalue = 4.91 (variance explained = 12%), factor three Eigenvalue = 5.29 (variance explained = 13%), factor four Eigenvalue = 5.10 (variance explained12 = %), factor five Eigenvalue = 3.32 (variance explained = 8%), factor six Eigenvalue = 1.57 (variance explained = 4%). The sixth factor identified by the parallel analysis had a substantially lower Eigenvalue and had one/no items loading onto it, therefore, a five-item solution was retained. Using a cut-off value of 0.40 [
58,
59], we found six items loaded onto factor one, six items on factor two, five items on factor three, one item on factor four, and no items loaded onto factor five. A factor with fewer than three items is considered weak and unstable [
62], therefore factors four and five were deleted and a three-factor solution was retained. The resulting 17-item GLiCS (v1.0) demonstrated good internal consistency (McDonald’s ω = 0.91). Each of the three factors constituted a meaningful subscale; (i)
access to resources, (ii)
belonging and contributing, and (iii)
independence; each of which also demonstrated good internal consistency (see Table
2). See Appendix
B for the full measure.
Table 2
Factor structure of the GLiCS (v 3.0)
I am able to get sufficient money to meet my basic needs (through employment or benefits) | 0.87 | 0.23 | -0.04 |
I am able to buy essential items for myself when I want to, for example clothes, toiletries or things for my home | 0.90 | -0.02 | 0.06 |
I am able to access the kind of food that I would like to eat | 0.80 | -0.03 | -0.07 |
I am able to access internet when I need to, for example on my phone or on a computer | 0.61 | -0.19 | 0.34 |
I am able to access courses to help build my skills and talents, for example art classes or dance classes | 0.55 | 0.14 | 0.16 |
I am able to choose which city and neighborhood I want to live in | 0.58 | 0.18 | 0.06 |
I am able to learn about my rights in this country, for example through support organisations | 0.17 | 0.48 | -0.06 |
I am able to feel I am a valued member of the community here | 0.11 | 0.46 | 0.01 |
When people around me are feeling sad, I feel able to support them and make them feel more positive | 0.05 | 047 | 0.9 |
I am able to rely on local organizations or charities for support with carrying out important tasks, for example paying bills or working through migration documents | 0.10 | 0.58 | -0.01 |
I am able to build a good life in this country | 0.04 | 0.79 | 0.08 |
I feel happy about being in this country | -0.02 | 0.78 | -0.24 |
I am able to read and write in the language of this country | 0.27 | -0.06 | 0.65 |
I am able to speak the official language(s) spoken in this country | 0.16 | -0.06 | 0.62 |
I am able to access green spaces in this country, for example parks or the countryside | -0.17 | 0.20 | 0.51 |
I am able to be involved in the decisions that affect my life, for example getting married or having children | 0.13 | -0.03 | 0.59 |
I am able to have my own privacy and keep information for myself if I want to, for example I can keep my bills and letters to myself | 0.06 | 0.04 | 0.63 |
McDonald’s Omega | 0.94 | 0.86 | 0.82 |
Convergent validity
The convergent validity of the GLiCS (v1.0) was tested using the WHO-5 [
53], the OxCAP-MH [
43] and the SIX [
52]. The overall scores for each measure can be found in Table
3. The GLiCS scores were correlated with wellbeing (WHO-5), capability-based wellbeing (OxCAP-MH), and with the SIX. Each of the subscales of the GLiCS were also correlated with the WHO-5, OxCAP-MH, and Six except subscale 2 which was not correlated with the SIX. For correlations see Table
4.
Table 3
The mean scores for each of the measures included in the analysis
SIX | 3.67 (1.4) |
OxCAP-MH | 66 (13.20) |
WHO-5 | 10.23 (6.55) |
Total GLiCS | 71.17 (12.33) |
Subscale 1: Access to Resources | 22.13 (6.94) |
Subscale 2: Belonging and Contributing | 22.86 (4.59) |
Subscale 3: Independence | 21.95 (3.30) |
Table 4
Correlations between each of the scales and subscales used to test convergent validity
1. SIX | - | | | | | | |
2. OxCAP-MH | 0.40* | - | | | | | |
3. WHO-5 | 0.43* | 0.54* | - | | | | |
4. Total GLiCS | 0.56* | 0.67* | 0.61* | - | | | |
5. Subscale 1: Access to Resources | 0.64* | 0.55* | 0.51* | 0.90* | - | | |
6. Subscale 2: Belonging and Contributing | 0.18 | 0.46* | 0.47* | 0.72* | 0.43* | - | |
7. Subscale 3: Independence | 0.42* | 0.57* | 0.47* | 0.70* | 0.53* | 0.30* | - |
Incremental validity
To test the incremental validity of the GLiCS (v1.0), a hierarchical regression was run to analyze the effects of age, migration status (refugee, asylum seeker or economic migrant), SIX, OxCAP-MH and GLiCS on levels of wellbeing (WHO-5). Age, migration status and SIX were entered into step one of the model. At step 2, the OxCAP-MH was added, and the GLiCS was entered in step three. Variance inflation factors suggested multicollinearity was not a concern. The final regression model was significant and explained 42.7% of variance (F(5, 85) = 14.39,
p < 0.001). Including the GLiCS (v1.0) at step three accounted for an additional 5.8% of variance in the model. Age, migration status and objective social outcomes (SIX) were not significant predictors of wellbeing (WHO-5). The OxCAP-MH (β = 0.25,
p = 0.025) and GLiCS (β = 0.36,
p = 0.003) were significant positive predictors of wellbeing. See Table
5.
Table 5
Hierarchical regression to test the effects of age, migration status, SIX, OxCAP-MH and GLiCS on levels of wellbeing (WHO-5)
Step 1 | .24 | 9.18*** | | |
Age | | | -.04 | .667 |
Migration Status | | | .18 | .050 |
Step 2 | .16 | 22.36*** | | |
SIX | | | .05 | .652 |
OxCAP-MH | | | .25 | .025 |
Step 3 | .06 | 9.61** | | |
GLiCS | | | .36 | .003 |
Lastly, a simple one-way between subject ANOVA was run to test the effect of migrant status (refugee, asylum seeker, economic migrant) on the GLiCS (v1.0) as a form of known groups validity [
51]. The assumption of homogeneity of variances was not met (
p < 0.001) therefore a Welch test was conducted. Welch’s test revealed a significant effect of migrant status on capability-based wellbeing (F(2, 47.77) = 26.92,
p < 0.001). Tamhane’s post hoc tests revealed a significant difference between economic migrants (M = 77.08, SD = 7.99) and both refugees (M = 69.29, SD = 13.90,
p = 0.007) and asylum seekers (M = 60.00, SD = 9.00,
p < 0.001).
There was also a significant difference between asylum seekers and refugees (
p = 0.009) with asylum seekers fairing worst on the GLiCS (v1.0) of all three groups followed by refugees and economic migrants respectively. These findings indicate that the GLiCS (v1.0) shows known groups validity for different migrant groups. The mean score on each of the three scales for each migrant group can be found in Table
6.
Table 6
Means, standard deviations, and p values for each subscale of the GLiCS depending on migration status
Subscale 1: Access to Resources | 14.68 ± 6.57 | 21.29 ± 7.08 | 25.85 ± 3.75 | < .001 |
Subscale 2: Belonging and Contributing | 21.26 ± 5.08 | 22.80 ± 5.11 | 23.44 ± 3.80 | .209 |
Subscale 3: Independence | 19.74 ± 3.94 | 21.07 ± 3.46 | 23.50 ± 1.91 | < .001 |
To determine whether this validity also exists for the OxCAP-MH, a simple one-way between subjects ANOVA was run to test the effect of migrant status (refugee, asylum seeker, economic migrant) on the OxCAP-MH. This analysis also revealed a significant effect of migrant status (F(2, 95) = 8.01, p = 0.001, ηp2 = 0.14). Fisher’s Least Significant Difference post hoc test revealed a significant difference between refugees (M = 62.37, SD = 13.71) and economic migrants (M = 71.32, SD = 12.22, p = 0.002), with refugees scoring lower. There was also a significant difference between asylum seekers (M = 59.38, SD = 9.2) and economic migrants (p = 0.001), with asylum seekers scoring lower. No significant difference was found between the refugee and asylum-seeking groups (p = 0.421).
Discussion
Over the last few years a number of calls have been made to expand the research focus in the area of migrant health to include psychosocial wellbeing and consideration of what factors may bring vitality to a person’s lived experiences [
2,
23,
24]. The primary aim of this study was to coproduce a capability-based wellbeing measure for migrant women in high-income settings. An assessment instrument of this type will facilitate the measurement of capabilities of migrant women, which can have important implications for monitoring their mental health and wellbeing, better understanding predictors of positive outcomes, and identifying areas that require further support and attention.
The study was divided into two phases. In phase I, an 88-itemversion of the Good Life in the Community Scale (GLiCS v0.1) was reduced and refined to a 42-item version (v0.2) through consultation with a migration expert advisory panel made up of refugee women who had experienced the asylum system, women working with migrant populations, and two researchers with previous experience of developing a capabilities-based outcome measure. In phase II, a parallel analysis and EFA were carried out, which suggested a three-factor solution for the GLiCS (v1.0; henceforth to as ‘the GLiCS’). Each of these three factors constitutes a GLiCS subscale: Access to resources (6 items), Belonging and contribution (6 items), and Independence (5 items).
The preliminary validation of the GLiCS showed promising psychometric properties including high internal consistency and good convergent validity. The concurrent validity of the GLiCS was tested through a correlation analysis with the SIX. A moderate positive correlation was found, providing evidence for the concurrent validity. Incremental validity was assessed by determining whether the GLiCS significantly increased the amount of variance in wellbeing scores beyond that of the SIX and the OxCAP-MH (controlling for age and migration status). This was indeed the case. Furthermore, evidence of known groups validity was obtained for the GLiCS, as the measure revealed significant difference between the different migrant groups. Unlike the GLiCS, the OxCAP-MH did not discriminate between refugees and asylum seekers in terms of levels of wellbeing, suggesting the GLiCS is a more appropriate instrument for measuring capability-based wellbeing of migrant women in high-income settings. Overall, the GLiCS demonstrated good psychometric properties in the current sample.
Adding to the work of Logelly et al. [
42], Greco et al. [
63] and Simon et al. [
43], the development of the GLiCS provides further evidence of the feasibility of operationalizing the CA in assessment of wellbeing. Importantly, the GLiCS is the first measure to be developed to measure capabilities in migrant populations. We believe that the three subscales that emerged from the data in the current study highlight the need to look across the different strata of the ecological model initially proposed by Bronfenbrenner [
64]. Bronfenbrenner proposed an ecological theory of human development which placed individuals within multiple interacting systems including intra-individual, interpersonal, and larger social systems. These systems have previously been applied to understanding the mental health of migrant groups [i.e. [
65‐
67]]. In the current study, the three scales highlight how a myriad of factors at different levels of the social environment of migrant women in the UK might affect their capabilities. When linking these subscales to Bronfenbrenner’s’ ecological model, the Access to Resources scale relates primarily to larger social systems, as the items within this subscale are influenced by the setting the individual finds themselves in (i.e., item 5; ‘
I am able to access courses to help build my skills and talents, for example art classes or dance classes’). The
Belonging and Contributing subscale speaks chiefly to the interpersonal system, i.e., pertaining to the social connections the individual can make within their community (i.e., item 2; ‘
I am able to feel I am a valued member of the community here’). Lastly, the
Independence subscale seems to relate to the intra-individual system i.e., the items speak to the person’s individual circumstances, sense of autonomy and agency (i.e., item 1. ‘
I am able to read and write in the language of this country’). As such, the subscales of the GLiCS can help to shed light on what capabilities are being satisfied and/or deprived across the different levels of female migrants’ ecology post-migration. Moving forward, this could help inform interventions and forms of support aimed at increasing wellbeing in these populations. This was recently discussed in more detail in a commentary on enhancing the capabilities of forcibly displaced populations [
24].
The development of the GLiCS can have important implications for policy and practice. Firstly, organizations (including non-governmental organizations and charities) supporting migrant women in high-income countries may benefit from using the GLiCS, as it can draw attention to specific issues that need to be addressed to support migrant wellbeing. It can also provide valuable information for advocacy efforts aimed at developing and amending policy and legislation relating to migration. Secondly, clinical services engaged in supporting the mental health and wellbeing of migrant women could benefit from using the GLiCS as an outcome measure to move beyond psychopathological outcomes and draw a more holistic picture of the individuals’ lived experience.
Strengths, limitations and future directions
A major strength of this study is that it reports on the empirical development of the first CA-specific psychometric scale to be developed for and validated in a migrant population. At each stage of the assessment instrument’s development (including the previous qualitative work; [
44] there was extensive involvement of experts by experience to ensure coproduction was facilitated. Following their participation, a number of participants provided positive feedback via e-mail to state that they had enjoyed participating and found the research highly relevant. A second strength of the study is the approach taken in the analyses. In previous studies researchers have erroneously used factor analyses developed for
interval-level data, when the construct itself is
ordinal in nature. To overcomes this specific statistical challenge, the current study used a polychoric correlation matrix [
68].
However, there are some limitations to the current study. The limited sample size means there is an increased likelihood of errors of inference regarding the factor structure of this scale [
62]. Best practice methods for EFA suggest a 10:1 subject to item ratio for EFA. This would suggest that for our initial 42-item GLiCS, a sample size of 420 was required. Given the challenges related to recruiting migrant women during the COVID19 pandemic, this desired sample size was not reached. As such, the conclusions presented here may not be generalizable beyond the current sample. Nonetheless, the EFA is designed and intended to be exploratory therefore the three-factor GLiCS presented in the current study can be used as a basis to conduct further analyses including confirmatory factor analysis, test–retest validity, and other latent variable modelling techniques that may help verify the proposed factor structure. This should also include exploring the association between the GLiCS with mental health measures such as the Patient Health Questionnaire (PHQ-9, [
69]) and/or the Generalized Anxiety Disorder measure (GAD-7, [
70]).If future research supports the psychometric properties of the GLiCS, then the assessment instrument could be used for evaluating the impact of resettlement and/or wellbeing interventions for migrants in high-income settings. This could provide insights into the benefits of interventions that go beyond health and basic resources, and instead provide a more holistic evaluation of wellbeing.
Beyond the limited sample size, the sample was also limited in terms of its representativeness of different migrant categories. This was particularly a concern for the EM given that the majority of EMs included in the sample came from the Netherlands and Spain. A recent report published by the Migration Observatory [
71] reported that workers from the EU-14 countries are more likely to be in high-skilled employment in the UK than those from new EU member states (EU-8 and EU-2), who are more likely to be in low-skilled occupations. For future research it would be valuable to include a question on type of job and income level particularly for EM, to ensure a representative sample is achieved for this group, and includes EM in jobs classified as lower skilled.
Furthermore, the reliance on online recruitment due to the COVID19 national restrictions potentially excluded participants that do not have access to the internet and/or a smartphone. It is possible that these participants may have more limited capabilities and face more significant barriers to achieving high levels of wellbeing than those represented in the current sample. Similarly, the focus on participants who speak English excluded people from the current study. A future direction for the current research could be to translate the measure into other languages (e.g. Arabic) for use with participants who do not have a strong command of the English language. This could provide important insights into groups who may have more limited capabilities post-resettlement due to language barriers. Overall, the GLiCS should be subject to replication studies using diverse and representative samples.
Future research can also focus on adapting this measure to different groups. For example, future research might develop assessment instruments (or indeed adapt the GLiCS) for assessing the wellbeing of male migrants, different age groups, time duration of the migration status or migrant women in low and middle-income settings. Additionally, longitudinal research designs may be used to see how capabilities change as individuals go through the asylum process and gain a refugee status within specific contexts. This would shed light on how capability priorities and freedoms may change over time. A further area for future research would be to explore the relationships between the capabilities identified in this thesis and specific functionings (i.e. feeling integrated within the community or having personal agency). Within the CA, the distinction between capabilities and functionings is between the effectively possible (capabilities) and the realized outcome (functionings). This would include understanding the freedoms and opportunities that migrants have to lead the kind of life that they have reason to value, and subsequently assess the functionings they end up with in their lives post-resettlement.
Conclusion
Our initial investigation into the psychometric properties of the GLiCS provides support for the internal consistency, validity and utility of the assessment instrument for assessing postmigration capability-based wellbeing for migrant women in high-income country settings. This is the first CA informed wellbeing scale to be developed and validated for use with migrant populations specifically. The three subscales found in the GLiCS (‘accessing resources’, ‘contributing and belonging’, and ‘independence’) highlight the different capability domains that are most relevant for migrant women to achieve high levels of wellbeing. The findings of this study provide further evidence of the merit, feasibility, and validity of operationalizing the CA for particular populations, and for applying the approach to outcome measure. The findings also highlight the relevance of developing a measure that speaks directly to the needs of migrant women in high-income settings.
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