The Enigma of Mental Health Intervention
Among the challenges that have surfaced as a result of the Covid-19 pandemic is the increasing prevalence of mental health cases within the population. Global research efforts have highlighted the need for immediate action to introduce mental health support in response to the pandemic. For example, a study by Hennein, Mew, & Lowe (2021) discusses how the pandemic has negatively impacted the mental health of healthcare workers in the United States, concluding that the prevalence of anxiety has increased 23.2% among healthcare workers within their study. The Government of Canada has recognized the significant impact of the pandemic on population mental health and the immediate need for mental health support, therefore investing $10.2 million in Covid-19 mental health and substance abuse research (“Government of Canada,” 2020).
When designing treatment programs for mental health management, physicians and program designers must first understand what factors influence mental health issues. Through this understanding strategies to manage the onset and/or treatment of mental illness can be introduced or shared as best practice. Many studies discuss the social determinants of health and the varying impacts upstream determinants and downstream determinants can have on health outcomes. Another method by which mental health has been researched is through the socio-ecological model (SEM) of health. The SEM demonstrates how factors part of multiple spheres of health contribute to the presentation of mental illness. This model takes factors identified by the social determinants of health and organizes them into various level health spheres to help researchers, policy designers, and governments understand the type of intervention needed for each sphere. The general spheres of the SEM include: Individual, Relationship, Community, Societal (figure 1) (Thompson et al., 2015). Using the SEM, research on mental health care and management has highlighted the importance of multilevel intervention in successful treatment plans (Oswald, 2020).
Individual
At the individual level factors include: Attitudes, Beliefs, and Personal History (Thompson et al. 2015). Personal ideologies about mental illness can affect the probability that an individual will seek out mental health information and/or support due to preconceived notions about mental illness that gives rise to self-stigma (Lannin, Vogel, Brenner, Abraham, & Heath, 2016). A study by Lannin et al. (2016) showed that undergraduate students with a high-level of self-stigma towards mental illness were half as likely to seek out mental health information and counseling. In order for an individual to access the help they need, they must be willing to accept support for their mental illness. However, if said individual has a high level of self-stigma regarding mental illness then they will be less likely to seek out this support. Ultimately, the individual thus risks the possibility of their condition further progressing (Corrigan, Druss, & Perlick, 2014; Lannin et al., 2016).
Relationship
In addition to the individual with mental illness, mental illness can also be taxing on the individual’s family and/or social network (Thompson et al., 2015). Family members often take on the role of informal caregivers, which can add burden, emotional distress, or make it difficult for family caregivers to hold other responsibilities such as a job (Thompson et al., 2015). In light of this, supporting the mental health of caregivers is key in supporting both the mental health of individuals living with mental illness who are supported by these caregivers and population mental health. In a qualitative study done by Thompson et al. (2010) caregivers of mental health patients indicated that support groups, respite care, and financial aid would provide them with the support they need to manage their own mental health.
Community
Cultural differences can impose a challenge in recognizing the signs of mental illness. Not only may some mental illnesses not be recognized by certain cultural groups, but illnesses like depression and anxiety may present differently between populations, therefore making it more difficult for health professionals to recognize signs of mental illness (Thompson et al., 2015). Additionally, members of minority cultural groups within a community may face communication challenges due to language barriers (Thompson et al., 2015). Groups such as refugees, who experience high levels of distress due to displacement-related stress would therefore struggle to access necessary mental health support (Thompson et al., 2015). As a result, Thompson et al. (2015) suggest community members and mental health professionals have access to language translation resources and adequate cultural sensitivity material in order to better serve the mental health needs of all members of a community.
Societal
Finally, systemic inequity due to lack of attention from governments can result in insufficient mental health resources, lack of information, and poor public policy (R. Jenkins, 2003). Jenkins et al. (2021) conclude that while individual-level strategies are important, policy intervention is necessary for effective and accessible mental health support. Populations experience challenges differently and therefore have different mental health outcomes. It has been widely noted through the Covid-19 pandemic that mental health has deteriorated more in certain populations, such as among indigenous groups or the older demographic (E. K. Jenkins et al., 2021). Therefore, it is important to understand the basis for these differences in impact and to design policies in such a way that they help address the needs of vulnerable populations – thus improving mental health equity.
After exploring the various levels of the SEM and how they influence mental health outcomes, it is clear to see the complexity of mental health issues. Such a complex health issue requires an equally complex solution. For example, normalizing mental illness through mental health literacy would help reduce levels of self-stigma and therefore increase the probability that someone would seek out necessary resources (Corrigan et al., 2014). That said, if stigma stems from systemic rather than intrapersonal issues, then mental health literacy may not be enough to address increasing rates of mental illness (Corrigan et al., 2014). Systemic problems may result in a lack of available mental health resources, and as a result, an individual may not be able to find the required resources even if they were to seek them out. By funding mental health resources and research, governments demonstrate their support, which is essential in the battle against mental illness. Focusing only on public policy intervention at the systemic level may not have a significant impact on each individual and puts a greater burden on government entities. Additionally, diffusion of these resources takes time and an individual living with mental illness may require immediate help. If a mental health prevention and management strategy were to increase mental health literacy for the individual and ensure policies and programs are in place to support those living with mental illness, this multilevel strategy would be more effective at managing population mental health compared to a strategy that targets a single level of influence.
In conclusion, the SEM highlights the influence that multiple spheres of health can have on mental health outcomes. Thus, in order to effectively design mental health support programs, these programs must aim to treat mental health from multiple levels of intervention. As discussed, by addressing individual influences of health such as self-stigma the direct mental health needs of an individual can be addressed immediately, providing the individual with quick relief. However, such quick fixes are not sustainable by themselves and should be combined with upper-level interventions, such as public policy support. All in all, using the SEM as a foundational tool to design treatment and prevention strategies helps shift mental health treatment from a reactive practice to a more proactive practice.
Figure 2. https://www.croakey.org/from-treatment-to-prevention-in-mental-health-a-socio-ecological-model/
References
Corrigan, P. W., Druss, B. G., & Perlick, D. A. (2014). The impact of mental illness stigma on seeking and participating in mental health care. Psychological Science in the Public Interest, Supplement, 15(2), 37–70. https://doi.org/10.1177/1529100614531398
Government of Canada and provincial partners support research addressing mental health impacts of COVID-19 on Canadians - Canada.ca. (2020, October 9). Retrieved March 2, 2021, from https://www.canada.ca/en/institutes-health-research/news/2020/10/government-of-canada-and-provincial-partners-support-research-addressing-mental-health-impacts-of-covid-19-on-canadians.html
Hennein, R., Mew, E. J., & Lowe, S. R. (2021). Socio-ecological predictors of mental health outcomes Among healthcare workers during the COVID-19 pandemic in the United States. PLOS ONE, 16(2). doi:10.1371/journal.pone.0246602
Jenkins, E. K., McAuliffe, C., Hirani, S., Richardson, C., Thomson, K. C., McGuinness, L., … Gadermann, A. (2021). A portrait of the early and differential mental health impacts of the COVID-19 pandemic in Canada: Findings from the first wave of a nationally representative cross-sectional survey. Preventive Medicine, 106333. https://doi.org/10.1016/j.ypmed.2020.106333
Jenkins, R. (2003). Supporting Governments to Adopt Mental Health Policies. World Psychiatry, 2(1), 14–19. https://doi.org/10.1108/13619322200500008
Lannin, D. G., Vogel, D. L., Brenner, R. E., Abraham, W. T., & Heath, P. J. (2016). Does self-stigma reduce the probability of seeking mental health information? Journal of Counseling Psychology, 63(3), 351–358. https://doi.org/10.1037/cou0000108
Oswald, T. (2020, October 12). From treatment to prevention in mental health: a socio-ecological model | Croakey. Retrieved March 2, 2021, from https://www.croakey.org/from-treatment-to-prevention-in-mental-health-a-socio-ecological-model/
Thompson, N.J., Walker, E.R., Obolensky, N., Winning, A., Barmon, C., Dilorio, C. & Compton, M.T. (2010). Distance delivery of mindfulness-based cognitive therapy for depression: project UPLIFT. Epilepsy and Behavior, 19(3), 247-254.
Thompson, N. J., McGee, R., Walker, E., & Munoz, L. (2015). Reflections on mental health advocacy across differing ecological levels. Journal of the Georgia Public Health Association, 5(1). doi:10.20429/jgpha.2015.050131
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