Young Adults Mental Health Services Experiences

Anxiety, Stress & Coping

Study Title: Young Adults’ Experiences with Mental Health Services

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Acknowledgements

I am highly grateful to my instructor, XXX, who has been an exceptional source of support throughout this study. Without his advice, ideas and corrections, this research would not have been possible. His knowledge, and continuous flexibility have ensured this study addresses all crucial details and his motivation has kept me on track from when I developed my first concept note to this final draft. I also acknowledge my colleague XXX at XXX College, who went out of their way to assist me in data entry, verification, and who was always there to offer review and advice related to data analysis. Finally, I acknowledge the contributions of XXX, the head librarian at XXX college, who offered immense support on relevant manuscripts that provided crucial insights on how to select, design, and arrange survey items. The expertise of each one of them helped in ensuring the success of this research study and in correcting many errors that would otherwise have gone unnoticed.

Abstract

This study sought to determine young adults’ experiences with the mental health services in their communities. Mental well-being for young adults is crucial as it influences their motivation levels, which, in turn, affects their academics, relationships, and ability to be productive citizens. Unfortunately, mental health problems for young adults continue to be overlooked and most of the existing services and studies are targeted at the general population, with no specific focus on the needs of young people. This quantitative study sought to address this gap by investigating the extent to which young people utilize the available mental health services, how satisfied they are with the existing services, and the obstacles that hinder them from effectively utilizing such services. The target population was college students aged between 18 and 25 in colleges in Florida. A sample of 150 participants was selected randomly and data collected using an online survey. Chi-square tests, odds ratios, and regression analyses were conducted to identify the dependence and strengths of associations between variables. The study found that on average, only 35% of young people are aware about the mental health services available in their communities, and 82% of these have sought counselling to address their mental health issues. Most young people prefer tele-counselling to conventional counselling as it is more convenient and less intrusive. Chi-square and odds ratio analyses show a statistically significant relationship between race, gender, and mode of study and mental health services utilization rates. Males, African-Americans and full-time students are more likely to seek mental health services. Generally, young people were dissatisfied with the quality of conventional and virtual counselling services they received. The most statistically significant barriers to effective utilization were the fear to be perceived as crazy, lack of information on when and where to seek care, lack of insurance and financial resources, preference for alternative mental health treatments, and fear of ruining one’s employment prospects. The study recommends expansion of tele-counselling services for the youth, raising awareness among young people on when and where to seek care, development of mental health safety net programs, enrolment of all eligible youth into Medicaid, and integration of mental health in hospitals to reduce stigmatization.

Key words: mental health, young people, counselling, access, utilization

Young Adults’ Experiences with Mental Health Services

Author

Department, College/University Name, Country Name

Introduction

The World Health Organization (WHO) defines mental health as \"a condition of well-being in which one understands his or her own potential, can cope with everyday stressors, work, and contribute to his or her community\" (Lee et al., 2015). Mental health wellbeing is a crucial aspect of young adults’ life as it can affect their academics due to low motivation, lack of focus, and isolation, and the ability to be well-rounded successful productive citizens. Many young people have poor mental health mainly due to academic stress, witnessing or experiencing Intimate partner violence (IPVP) in their homes, social disadvantage, abuse, abandonment, and bullying (Kirker et al., 2022). Anxiety and depression are the two main reported mental health challenges that are associated with this group. Following the COVID-19 pandemic, the number of students seeking mental health services increased due to various disruptions that increased their vulnerability to psychological distress (Lee et al., 2021). Students were forced to abandon their classrooms and focus on e learning, which was quite difficult especially for the first few months. Many students struggled to stay engaged while others had to lose their employment, part time jobs, and socializing opportunities.

Prior to the pandemic, very few studies focused on the mental health of students and young adults as the focus is mainly on older people. Even though youths’ mental health problems are often overlooked, if effective intervention is not provided, many wind up with lasting harmful effects. Some develop behavioral problems, suicidal ideation, intrusive thoughts, and substance abuse, antisocial behaviors that prevent them from being socially competent individuals (Appleton et al., 2021). In some extreme cases, the youths commit suicide especially if they feel extreme stress, or experience traumatic experience like rape, cyber bullying, neglect or physical abuse.

Over the years, many studies focused on mental health services that are provided for adults without considering the needs of young adults (Meherali, et al., 2021). However, during the COVID-19 pandemic, the issue of mental health problem among youths gained more attention because of isolation that worsened the symptoms. The lockdowns that were recommended by the CDC caused many youths to stay indoors without any form of one-on-one interaction with friends. Many relied too much on social media, which caused some to develop body image issues, depression and anxiety, and bullying or suicidal ideation (Hollis, 2022). Academic stress was also a major issue especially because students were forced to adapt fast to using technologies in the new environment. Many longitudinal studies that were conducted during this period showed that students’ wellbeing had worsened due to the pandemic. The students were more likely to experience depression or high anxiety levels compare to their wellbeing before he pandemic. The studies however did not show accessibility of mental health services at schools because the students were on lockdown during the pandemic period.

Although some students adapted fast, many are continuing to recover from the negative consequences that the pandemic had on their mental wellbeing. Unfortunately, there were limited mental health resources where such youths could seek support. Many could not access the services due to the long waits, because the clinic specialists were unable to keep up with the surge in demand (Hollis, 2022). Others relied on virtual therapy or online therapy although the effectiveness of such services had not been thoroughly researched.

In addition to lack of adequate resources and difficulty accessing mental health services, studies show that other factors such as stigmatization of mental health problems, poor interactions with the mental health practitioners, misconceptions and feelings of judgment, and feelings of shame among others prevent youths from seeking mental health services (Appleton et al., 2021). Consequently, they continue experiencing increased trauma symptoms, depression and anxiety levels among other mental health symptoms. Given the limited amount of research that has been conducted focusing on youth’s mental health, it is crucial to examine the needs of the group to ensure that resources are made available for the group. This means first identifying the challenges that these youths encounter when trying to seek mental health services through both the conventional means and digital technologies to design better systems that support youths.

Recent studies show that little attention has been paid to mental health services available to young people despite the lasting impacts of mental health problems (Kirker, et al., 2022). Evidence clearly show that students experienced adverse psychological impacts following the pandemic, but there is limited information showing their utilization of mental health services. This study thus tries to bridge the existing gap by investigating the challenges that youths face when trying to access mental health services both online and offline. Some may have a negative attitudes towards such services, might not be aware that they exist or might not think that they are a priority. Others may have resorted to unreliable virtual services to seek help without knowing their effectiveness. It helps to recommend the most viable solutions to ensure that youths receive effective support services to improve their wellbeing. The research could for instance reveal the most effective digital services that could be recommended to youths. It could also give an insight into designing interventions or approaches that are tailored specifically to meet the needs of youths.

This study pursues three main objectives:

i) To determine the extent to which college students and young adults with mental health issues utilize support on their campuses and elsewhere

ii) To determine the level of satisfaction with mental health services among college students and young adults

iii) To identify the obstacles or barriers that hinder young people from utilizing mental health services both virtually and through conventional means

Materials and Methods

Study Design

The study adopts a cross-sectional research design, where data is collected from sampled participants at a single point in time. Quantitative data was gathered using an online survey in form of a questionnaire, which made it possible to get more respondents and more honest views from participants. Unlike an interview, questionnaires allow anonymity, which means there is no pressure to give socially desirable answers. A large sample size would also enable generalization. The positivist paradigm, which views reality as objective as opposed to subjective, will be used in this study. The underlying premise is that an objective reality exists and that knowledge is not derived from human perceptions but rather from observation and experimentation (Park et al., 2020). Quantitative research tends to be more in line with positivist paradigm.

Sampling Frame

The target population for the study was college students in Florida. Due to the large size of the population, four college campuses were selected to take part in the study. The four were selected due to their geographical proximity to the researcher, which minimized costs in the distribution of flyers. The sampling frame was a list of all students enrolled in the four participating college campuses.

Sampling Method

Participants were selected through simple random sampling and were recruited both online and offline. Flyers were sent across different campuses asking students who experience mental health challenges to participate. Various online platforms like social media were used to reach students in different campuses. Those interested in participating were advised to contact the researcher on the email provided in the flyer. Two hundred participants were selected randomly from the 250 who expressed interest. The 200 received consent forms via email, and were to indicate their agreement to the study terms and conditions before sending the same back to the researcher. A total of 180 signed consent forms were received, and the participants received a Google Forms link to the survey. The survey contained questions on the students’ experiences with mental health support service. They were, for instance, asked to rate their satisfaction levels with the interactions that they had with the mental health specialists. They were also be asked to assess the effectiveness of the interventions they receive virtually and conventionally.

Research Question and Hypothesis

The study seeks to answer three research questions:

RQ1: To what extent do college students and young adults with mental health issues utilize support services on their campuses and elsewhere?

1. H0: on the average, 50% of young people are aware of the mental health services available in their communities

Ha: on the average, awareness levels on available mental health services among the youth is not 50%

2. H0: on average, 50% of young people have utilized either conventional or tele-counselling when faced with mental health issues

Ha: on the average, utilization rates for conventional or tele-counselling among the youth are not 50%

3. H0: there is no significant relationship between mental health services utilization and demographics (gender, mode of study, sexual orientation)

Ha: there is a significant relationship between mental health services utilization and demographic characteristics (gender, mode of study, sexual orientation)

4. H0: there is no significant relationship between mental health score and mental health services utilization among young people

Ha: there is a significant relationship between mental health score and mental health services utilization among young people

RQ2: What is the level of satisfaction with mental health services among college students and young adults?

5. H0: on average, young people are not satisfied with the mental health services they receive

Ha: on average, young people are highly satisfied with the mental health services they receive

RQ3: What obstacles or barriers do young people face when attempting to obtain mental health services both virtually and through conventional means?

6. H0: there is no significant linear relationship between the selected barriers (being perceived as weak/lack of information on when and where to seek care/ lack of insurance and financial resources/ fear of losing children’s custody/ preference for alternative treatments/and fear of ruining employment prospects) and mental health services utilization among young people

Ha: there is a significant linear relationship between the identified barriers and mental health services utilization among young people

Data Collection

The researcher obtained 150 survey responses that were included in the final analysis. Participants could access the survey from their smartphones and save progress to continue later in case they did not have time to complete at once. Responses were received in the researcher’s email once the participants submitted their responses by clicking on the Submit button at the end of the survey. This ensured anonymity as responses received could not be traced to a particular email.

The survey comprised of closed ended questions that required respondents to indicate their most preferred options on a 5-point Likert Scale. Participants indicated how frequently they experienced selected mental health stressors and how frequently they experienced obstacles to seeking mental health services on a scale of four (always) to 1 (rarely). They also indicated their level of satisfaction with available mental health services on a scale of 4 (Very Satisfied) to 1 (Dissatisfied). The scale included a ‘Prefer not to Say’ category to enhance objectivity and ensure that neutral participants were not compelled to give untrue responses. At the end of the survey, the respondents were asked an open-ended question requiring them to provide suggestions and any other details they feel would be crucial in enhancing mental health services for young people.

Data Analysis Plan

The data was analyzed using the Statistical Package for Social Sciences (SPSS). Descriptive analysis was used to analyze the first and second research questions. Regression analysis was used to determine the associations between mental health services utilization rates and each of the selected barriers or obstacles obtained from the literature to determine which barriers had a significant effect on support services utilization among young people. Thematic analysis was used to identify the recurring patterns or ideas from the data on ways of enhancing young people’s experiences with mental health services. The most salient ideas and themes will be adopted as part of the policy recommendations.

Results

Demographic Statistics

Demographic variables included gender (0=male and 1=female), Racial identity (0=black, 1=non-white Latino, 2=white, 3=Asian), sexual orientation (0=non-LGBTQ individual, 1=LGBTQ individual), Mode of study (0=full-time, 1=part-time), Income, and Employment Status (0=Not employed, 1=Employed). ‘Preferred not to Say’ responses were coded as missing values.

27.6 percent of respondents were male, while 33.1were female. In terms of racial identity, Blacks were the majority of respondents, at 38.7 percent as compared to 8.3 percent for whites, 11 percent for non-white Latino, and 2.8 percent for Asians. 7.7 percent of respondents identified themselves as LGTBQ individuals, as compared to 53 percent non-LBTQ individuals. In the area of study, 69.6 percent of respondents were full-time students, while 7.2 percent were part-timers.

The maximum annual income among the 14 respondents who declared their incomes was 30,000 and the minimum was 12,000, leading to a mean income of $18,846.15 annually and a standard deviation of 4,793 as shown in table 1 below. The standard deviation is significantly lower than the mean, implying that most incomes are clustered around the mean.

Table 1. Descriptive Statistics: Annual Income

N

Minimum

Maximum

Mean

Std. Deviation

Annual Income Score

13

12000

30000

18846.15

4793.158

Valid N (listwise)

13

The average mental illness score as measured by the presence of the six stressors was 15.473 as shown in table 2 (SD=3.613). The heathiest respondent obtained a score of 4 on the frequency of stressors, while the least healthy obtained a score of 22, where 24 represents the highest possible score and the highest likelihood of mental illness. This implies that most of the respondents face a high likelihood of mental illness from stressors related to high academic expectations, problems with lectures, homework/examination stress, isolation, relationships, and family problems.

Table 2. Descriptive Statistics: Mental Illness Score

N

Minimum

Maximum

Mean

Std. Deviation

Mental Illness

150

4.00

22.00

15.4733

3.61335

Valid N (listwise)

150

RQ1: To what extent do college students and young adults with mental health issues utilize support services on their campuses and elsewhere?

This research question was answered using three survey items: Have you utilized any counseling services for mental health, are you aware of available mental health services in your community, and have you utilized tele-counselling. Using a score of 0 for No and 1 for Yes, frequency analysis showed that on the average, 65 percent of college students were not aware of mental health services available in their communities (see Table 3 below). We thus accept the first null hypothesis, which states that only 50% of young people are aware about the mental health services available in their communities.

Table 3. Are you aware of available mental health services in your community?

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

.00

98

65.3

65.3

65.3

1.00

52

34.7

34.7

100.0

Total

150

100.0

100.0

Missing

System

0

0.00

Total

150

100.0

However, 82 percent of college students reported that they had used counselling services for mental health issues at some point (see table 4). With this finding, we reject the second null hypothesis, which states that on average, only 50 percent of young people have utilized counselling services to address their mental health issues.

Table 4. Have you utilized any counselling services for mental health?

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

.00

26

17.3

17.3

17.3

1.00

124

82.7

82.7

100.0

Total

150

100.0

100.0

Missing

System

0

0.00

Total

150

100.0

The relatively high utilization rates for counselling point to counselling as the preferred treatment option for mental illness among young people. Interestingly, 76 percent of young people who sought counselling services reported using tele-counselling, indicating that young people preferred tele counselling to conventional counselling (see table 5 below). With this finding, we reject the second null hypothesis, which states that on average, 50 percent off young people have used tele-counselling services, and accept the alternative hypothesis.

Table 5. Have you used tele-counselling?

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

.00

10

5.5

6.7

6.7

1.00

139

76.8

93.3

100.0

Total

149

82.3

100.0

Missing

System

32

17.7

Total

181

100.0

Chi-square tests were conducted to determine whether utilization of mental health services was dependent on any of the main demographic variables. Odds ratio analyses were conducted besides the chi-square tests to test the strength of the relationship for variables that yielded significant chi-square results. Results of chi-square tests show some significant dependence between both gender and racial identity and the utilization of counselling services for mental health problems. Both demographic variables report a Person chi-square value less than P=0.05. The chi-square and odds ratio tables are presented in the appendices section.

Tables 12 to 14 in the appendices section show that males and blacks are more likely than females and other racial categories to seek counselling services when faced with mental health issues. Odds ratio for the variable gender shows that the 95% confidence interval value is less than 1 (95% CI = 0), indicating a statistically significant relationship between gender and mental health services utilization (see table 12). Thus, we reject the third null hypothesis, which states that no statistically relationship exists and accept the alternative hypothesis. The odds ratio (see table 12) equals 1.62, implying that males are 1.62 times more likely to seek mental health services than females.

In regard to race, the 95% confidence interval is less than 1, indicating that race has a statistically significant influence on whether one utilizes mental health services. Thus, we reject the third null hypothesis, which states that no statistically significant relationship exists and accept the alternative hypothesis. The odds ratio (see table 13) equals 2.043, indicating that, as suggested by the social stress theory, African-Americans are 2 times more likely than other racial categories to seek help for mental health services. The chi-square tests of independence yield insignificant results for both mode of study and sexual orientation, indicating that their influence on whether one utilizes counselling services is minimal, contrary to the social stress hypothesis.

However, mode of study significantly influences the likelihood of utilizing tele-counselling, with full-time students reporting higher utilization rates than their part-time counterparts (see table 14 in the appendices). Odds ratio analysis shows a 95% confidence interval greater than 1 and a significance level of p=0.01, indicating that the relationship is statistically significant. Thus we reject the third null hypothesis, which states that no significant relationship exists, and accept the alternative hypothesis. The odds ratio (see table 14) equals 18.2, implying that full-time students are 18 times more likely to use tele-counselling services than their part-time counterparts. A possible reason for this is that full-time students have more access to institutional resources, including internet connectivity and institution-based tele-counselling facilities that may not be accessible by part-timers. All other demographic variables are independent of the decision to use tele-counselling services.

Odds ratio analysis was conducted to test the relationship between mental health score (cumulative score on the mental health stressors) and counselling utilization rates. The odds ratio equals 0.842 and the 95% confidence interval value is less than 1, implying that the relationship between the two variables is statistically significant. Thus, we reject the fourth null hypothesis, which states that no significant relationship exists between the variables, and accept the alternative hypothesis. The odds ratio is less than 1, indicating that students with the most mental health issues are the least likely to seek out counselling services when faced with stressors (see table 6). A possible reason is that young people may not wish to be associated with mental health issues for the fear of retribution and the effect of such association on how one is perceived by peers.

Table 6. Odds Ratio Table for Variables in the Equation

Mental Health Score against Utilization Rates

B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1a

MentalHealthScore

-.172

.076

5.157

1

.023

.842

.726

.977

Constant

4.332

1.283

11.409

1

.001

76.098

a. Variable(s) entered on step 1: MentalHealthScore.

RQ2: What is the level of satisfaction with mental health services among college students and young adults?

The level of satisfaction was measured using two survey items: what is your level of satisfaction with the counselling services received (conventional counselling) and how would you rate the virtual services received (tele-counselling). The frequency table for the overall satisfaction with conventional counselling services received is presented as table 7 below.

Table 7. What is your Level of Satisfaction with the Conventional Counselling Services Received?

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1.00

49

32.6

32.7

32.7

2.00

55

36.7

36.7

69.3

3.00

32

21.3

21.3

90.7

4.00

11

7.3

7.3

98.0

5.00

3

2.0

2.0

100.0

Total

150

100.0

100.0

Missing

System

0

Total

150

100.0

Using a score of 1 to represent the lowest satisfaction level and 5 the highest satisfaction level on a scale of 1 to 10, 32 percent reported the least satisfaction level (between 2 and 4), 36.7 percent reported a satisfaction level of between 4 and 6, and only 2 percent reported that they were completely satisfied with the counselling services received.

In regard to tele-counselling services, the results are as presented in table 8 below:

Table 8. How would you rate your overall satisfaction with tele-counselling services received?

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1.00

67

48.2

48.2

48.2

2.00

9

6.5

6.5

54.7

3.00

37

26.6

26.6

81.3

4.00

26

18.7

18.7

100.0

Total

139

100

100.0

Missing

System

0

0

Total

150

100.0

48 percent of participants reported general dissatisfaction with the tele-counselling services received, 6.5 percent were indifferent about the quality of services, 26.6 percent were satisfied, and 18 percent reported that they were very satisfied with the services they received.

Asked whether they felt the services received (both conventional and virtual) met the needs of young people, the results were as presented in table 9.

Table 9. Do you believe the mental health services offered met the needs of young people?

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

.00

98

65.3

65.3

34.7

1.00

52

34.7

34.7

100.0

Total

150

100.0

100.0

Missing

System

0

0.0

Total

150

100.0

Using a score of 0 to represent ‘No’ and 1 to represent ‘Yes’, 65 percent of young people believe that the services offered both conventionally and virtually do not meet the needs of young people. The results thus satisfy the fifth null hypothesis that generally, young people are dissatisfied with the available mental health services as they feel that these are not structured to address their needs.

RQ4. What obstacles or barriers do young people face when attempting to obtain mental health services both virtually and through conventional means?

A linear regression was carried out to determine the extent to which each of the six identified barriers predicted counselling services utilization rates among young people. Table 10 below presents the results of linear regression carried out on six barriers identified from the literature.

Table 10. Regression Coefficients for Perceived Barriers to Mental Health Services Utilization

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

(Constant)

2.818

.457

6.171

.000

I will be perceived as weak and crazy

.057

.049

.104

1.164

.046

Lack information on when and where to seek care

-.122

.058

-.272

-2.099

.038

I lack financial resources and insurance cover

-.222

.051

-.326

-4.333

.000

Fear of losing my child’s custody

-.033

.041

-.075

-.804

.423

Preference for alternative treatments

.000

.064

.000

.002

.023

Fear of ruining employment prospects

-.229

.049

-.393

-4.648

.000

a. Dependent Variable: Counselling Utilization Rates

Linear regression results show that five of the six barriers significantly influence mental health services utilization rates among young people. The five are the fear of being perceived as weak/crazy, lack of information on when and where to get help, lack of insurance and financial resources, preference for alternative treatments such as meditation, and fear that having a mental illness record would ruin one’s chances of employment. For these five independent variables, we reject the null hypothesis, which states that no statistically significant relationship exists, and accept the alternative hypothesis. The fear of losing children’s custody yielded insignificant results, and we thus accept the null hypothesis. Based on the beta coefficients, lack of information on when and where to seek care, lack of financial resources/insurance coverage, and fear of ruining one’s employment prospects have the greatest influence on mental health services utilization among young people.