Young Adults' Mental Health Services: Access, Use & Barriers

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 well-being is a crucial aspect of young adults' lives, as it can affect their academic performance through low motivation, lack of focus, and social isolation, as well as their ability to become well-rounded, productive citizens. Many young people experience poor mental health primarily due to academic stress, witnessing or experiencing intimate partner violence (IPV) in their homes, social disadvantage, abuse, abandonment, and bullying (Kirker et al., 2022). Anxiety and depression are the two most commonly reported mental health challenges associated with this group.

Following the COVID-19 pandemic, the number of students seeking mental health services increased due to various disruptions that heightened their vulnerability to psychological distress (Lee et al., 2021). Students were forced to abandon in-person classrooms and transition to e-learning, which proved difficult — especially during the first few months. Many students struggled to stay engaged, while others lost 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 research focus was primarily on older populations. Even though mental health problems among young people are often overlooked, a lack of effective intervention can lead to lasting harmful effects. Some young people develop behavioral problems, suicidal ideation, intrusive thoughts, substance abuse, and antisocial behaviors that prevent them from becoming socially competent adults (Appleton et al., 2021). In extreme cases, youth may attempt or complete suicide, particularly when they experience severe stress or traumatic events such as rape, cyberbullying, neglect, or physical abuse.

Over the years, many studies have focused on mental health services provided for adults without considering the distinct needs of young adults (Meherali et al., 2021). However, during the COVID-19 pandemic, youth mental health gained greater attention because isolation worsened symptoms. Lockdowns recommended by the CDC caused many young people to remain indoors without face-to-face interaction. Many relied heavily on social media, which contributed to body image issues, depression, anxiety, cyberbullying, and suicidal ideation in some cases (Hollis, 2022). Academic stress was also a significant issue, as students were required to rapidly adapt to technology-based learning environments. Longitudinal studies conducted during this period consistently found that student well-being had worsened as a result of the pandemic; students were more likely to experience depression or elevated anxiety compared to pre-pandemic levels. These studies did not, however, examine access to mental health services, since students were under lockdown during the pandemic period.

Although some students adapted quickly, many continue to recover from the negative consequences the pandemic had on their mental well-being. Unfortunately, mental health resources for these young people were limited. Many could not access services due to long wait times, as clinic specialists were unable to keep up with the surge in demand (Hollis, 2022). Others turned to virtual or online therapy, although the effectiveness of such services had not been thoroughly researched at the time.

In addition to inadequate resources and access difficulties, research shows that other factors — including stigmatization of mental health problems, poor interactions with practitioners, misconceptions, feelings of being judged, and shame — prevent young people from seeking mental health services (Appleton et al., 2021). As a result, many continue to experience worsening trauma symptoms, depression, and anxiety. Given the limited research focused specifically on youth mental health, it is critical to examine the needs of this group to ensure that appropriate resources are made available. This means first identifying the challenges young people encounter when seeking mental health services — both through conventional means and digital technologies — in order to design better support systems.

Recent studies confirm that little attention has been paid to mental health services available to young people, despite the lasting impacts of untreated mental health problems (Kirker et al., 2022). Evidence clearly shows that students experienced adverse psychological impacts following the pandemic, yet there is limited information on their utilization of mental health services. This study seeks to bridge that gap by investigating the challenges young people face when trying to access mental health services both online and offline. Some may hold negative attitudes toward such services, may be unaware that they exist, or may not consider them a priority. Others may have turned to unreliable virtual services without knowing their effectiveness. The research aims to recommend viable solutions to ensure that young people receive effective support to improve their well-being — for instance, by identifying the most effective digital services and informing the design of interventions tailored specifically to youth needs.

This study pursues three main objectives:

(i) To determine the extent to which college students and young adults with mental health issues utilize support services 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

The study adopts a cross-sectional research design, in which data is collected from sampled participants at a single point in time. Quantitative data were gathered using an online survey in the form of a questionnaire, which enabled a larger number of respondents and more candid responses. Unlike interviews, questionnaires allow anonymity, removing pressure to give socially desirable answers. A large sample size also enables generalization of findings. The positivist paradigm — which views reality as objective rather than subjective — underpins this study. The underlying premise is that an objective reality exists and that knowledge is derived from observation and experimentation rather than from human perception alone (Park et al., 2020). Quantitative research is generally well-aligned with the positivist paradigm.

The target population was college students in Florida. Due to the size of this population, four college campuses were selected to participate in the study. These four campuses were chosen because of their geographical proximity to the researcher, which minimized costs associated with distributing recruitment flyers. The sampling frame consisted of a list of all students enrolled at the four participating campuses.

Participants were selected through simple random sampling and were recruited both online and offline. Flyers were distributed across different campuses inviting students who experience mental health challenges to participate. Online platforms, including social media, were used to reach students at various campuses. Interested participants were directed to contact the researcher via the email address provided on the flyer. Two hundred participants were selected randomly from the 250 who expressed interest. These 200 individuals received consent forms via email and were required to indicate their agreement with the study's terms and conditions before returning the signed forms to the researcher. A total of 180 signed consent forms were received, and those participants were sent a Google Forms link to the survey. The survey contained questions about students' experiences with mental health support services. Participants were asked, for example, to rate their satisfaction with interactions with mental health specialists and to assess the effectiveness of interventions received both virtually and conventionally.

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

H0₁: On average, 50% of young people are aware of the mental health services available in their communities.
Ha₁: On average, awareness levels of available mental health services among youth are not 50%.

H0₂: On average, 50% of young people have utilized either conventional or tele-counselling when faced with mental health issues.
Ha₂: On average, utilization rates for conventional or tele-counselling among youth are not 50%.

H0₃: There is no significant relationship between mental health services utilization and demographics (gender, mode of study, sexual orientation).
Ha₃: There is a significant relationship between mental health services utilization and demographic characteristics (gender, mode of study, sexual orientation).

H0₄: There is no significant relationship between mental health score and mental health services utilization among young people.
Ha₄: 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?

H0₅: On average, young people are not satisfied with the mental health services they receive.
Ha₅: 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?

H0₆: There is no significant linear relationship between the selected barriers (fear of being perceived as weak or crazy; lack of information on when and where to seek care; lack of insurance and financial resources; fear of losing child custody; preference for alternative treatments; and fear of ruining employment prospects) and mental health services utilization among young people.
Ha₆: There is a significant linear relationship between the identified barriers and mental health services utilization among young people.

The researcher obtained 150 survey responses that were included in the final analysis. Participants could access the survey from their smartphones and save their progress to continue later if they could not complete it in one sitting. Responses were received in the researcher's email upon submission. This ensured anonymity, as submitted responses could not be traced to a particular email address.

The survey comprised closed-ended questions requiring respondents to select their preferred options. Participants indicated how frequently they experienced selected mental health stressors and how frequently they encountered obstacles to seeking services on a scale from 4 (always) to 1 (rarely). They also rated their satisfaction with available mental health services on a scale from 4 (very satisfied) to 1 (dissatisfied). The scale included a "Prefer not to Say" category to enhance objectivity and ensure that participants were not compelled to provide responses they did not genuinely hold. At the end of the survey, respondents were asked an open-ended question inviting suggestions and additional details they felt would be useful for improving mental health services for young people.

Data were analyzed using the Statistical Package for Social Sciences (SPSS). Descriptive analysis was used to address 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 identified from the literature, in order to determine which barriers had a significant effect on service utilization. Thematic analysis was used to identify recurring patterns and ideas from open-ended responses regarding ways to enhance young people's experiences with mental health services. The most salient themes were incorporated into the policy recommendations.

Results

Demographic variables included gender (0 = male, 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). "Prefer not to Say" responses were coded as missing values.

Among respondents, 27.6% were male and 33.1% were female. In terms of racial identity, Black respondents were the majority at 38.7%, compared with 8.3% white, 11% non-white Latino, and 2.8% Asian. LGBTQ individuals accounted for 7.7% of respondents, compared with 53% non-LGBTQ individuals. Regarding mode of study, 69.6% of respondents were full-time students and 7.2% were part-time students.

Among the 13 respondents who disclosed their income, the maximum annual income was $30,000 and the minimum was $12,000, yielding a mean of $18,846.15 (SD = $4,793.16). The standard deviation is significantly lower than the mean, indicating that most reported incomes are clustered around the mean.

Table 1. Descriptive Statistics: Annual Income

N = 13 | Minimum = $12,000 | Maximum = $30,000 | Mean = $18,846.15 | Std. Deviation = $4,793.16

The average mental illness score — measured by the combined frequency of six stressors — was 15.47 (SD = 3.61), as shown in Table 2. The healthiest respondent scored 4 on stressor frequency, while the least healthy scored 22 out of a possible maximum of 24. This indicates that most respondents face a high likelihood of mental health difficulties stemming from stressors related to high academic expectations, problems with lecturers, homework and examination stress, isolation, relationship difficulties, and family or work problems.

Table 2. Descriptive Statistics: Mental Illness Score

N = 150 | Minimum = 4.00 | Maximum = 22.00 | Mean = 15.4733 | Std. Deviation = 3.61335

This research question was addressed using three survey items: whether participants had utilized any counselling services for mental health; whether they were aware of available mental health services in their community; and whether they had used tele-counselling. Using a score of 0 for "No" and 1 for "Yes," frequency analysis revealed that 65% of college students were unaware of mental health services available in their communities (see Table 3). Accordingly, the first null hypothesis — that only 50% of young people are aware of available mental health services — is accepted.

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

No (0.00): 98 respondents (65.3%) | Yes (1.00): 52 respondents (34.7%) | Total: 150 (100%)

However, 82% of college students reported having used counselling services for mental health issues at some point (see Table 4). Based on this finding, the second null hypothesis — that on average only 50% of young people have utilized counselling services — is rejected.

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

No (0.00): 26 respondents (17.3%) | Yes (1.00): 124 respondents (82.7%) | Total: 150 (100%)

The relatively high utilization rates for counselling point to it as the preferred treatment option for mental illness among young people. Notably, 76% of those who sought counselling reported using tele-counselling, indicating that young people prefer tele-counselling over conventional counselling (see Table 5). The second null hypothesis regarding tele-counselling utilization rates is therefore rejected in favor of the alternative hypothesis.

Table 5. Have you used tele-counselling?

No (0.00): 10 respondents (6.7%) | Yes (1.00): 139 respondents (93.3%) | Valid Total: 149

Chi-square tests were conducted to determine whether utilization of mental health services was associated with any of the main demographic variables. Odds ratio analyses were also conducted for variables that yielded significant chi-square results, in order to assess the strength of those relationships. Results of chi-square tests indicate statistically significant associations between both gender and racial identity and the utilization of counselling services (Pearson chi-square p < 0.05 for both variables). The full chi-square and odds ratio tables are presented in the appendices.

Results show that males and Black respondents are more likely than females and other racial categories to seek counselling services for mental health issues. The 95% confidence interval for gender is less than 1 (95% CI = 0), indicating a statistically significant relationship between gender and mental health services utilization. The third null hypothesis is therefore rejected. The odds ratio equals 1.62, implying that males are 1.62 times more likely to seek mental health services than females.

Regarding race, the 95% confidence interval is also less than 1, indicating that racial identity has a statistically significant influence on mental health services utilization. The third null hypothesis is again rejected. The odds ratio equals 2.043, indicating that — consistent with the social stress theory — African Americans are approximately 2 times more likely than other racial groups to seek mental health services. Chi-square tests of independence yielded insignificant results for both mode of study and sexual orientation in relation to conventional counselling utilization, indicating minimal influence from these variables.

However, mode of study significantly influences tele-counselling utilization, with full-time students reporting higher 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. The third null hypothesis is rejected. The odds ratio equals 18.2, meaning full-time students are 18 times more likely to use tele-counselling than part-time students. A likely explanation is that full-time students have greater access to institutional resources, including internet connectivity and campus-based tele-counselling facilities that may not be accessible to part-time students. All other demographic variables were found to be independent of the decision to use tele-counselling.

An odds ratio analysis was also conducted to test the relationship between mental health score (the cumulative score on mental health stressors) and counselling utilization rates. The odds ratio equals 0.842, with a 95% confidence interval less than 1, indicating a statistically significant relationship. The fourth null hypothesis is therefore rejected. The odds ratio below 1 indicates that students with the most mental health difficulties are the least likely to seek counselling services (see Table 6). A possible explanation is that young people may avoid association with mental health issues for fear of social retribution or the stigma attached to being perceived as mentally ill by peers.

Table 6. Odds Ratio — Mental Health Score vs. Utilization Rates

MentalHealthScore: B = −0.172 | S.E. = 0.076 | Wald = 5.157 | df = 1 | Sig. = 0.023 | Exp(B) = 0.842 | 95% C.I.: Lower = 0.726, Upper = 0.977

Constant: B = 4.332 | S.E. = 1.283 | Wald = 11.409 | df = 1 | Sig. = 0.001 | Exp(B) = 76.098

Satisfaction was measured using two survey items: overall satisfaction with conventional counselling services received, and overall satisfaction with tele-counselling services received.

Table 7. Level of Satisfaction with Conventional Counselling Services Received

Score 1.00 (lowest): 49 (32.7%) | Score 2.00: 55 (36.7%) | Score 3.00: 32 (21.3%) | Score 4.00: 11 (7.3%) | Score 5.00 (highest): 3 (2.0%) | Total: 150

Using a scale of 1 (lowest satisfaction) to 5 (highest satisfaction), 32% of respondents reported the lowest satisfaction level, 36.7% reported moderate satisfaction, and only 2% reported being completely satisfied with conventional counselling services received.

Table 8. Overall Satisfaction with Tele-Counselling Services Received

Score 1.00 (dissatisfied): 67 (48.2%) | Score 2.00: 9 (6.5%) | Score 3.00: 37 (26.6%) | Score 4.00 (very satisfied): 26 (18.7%) | Total: 139

Regarding tele-counselling, 48% of participants reported general dissatisfaction with the services received, 6.5% were indifferent, 26.6% were satisfied, and 18.7% reported being very satisfied.

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

No (0.00): 98 (65.3%) | Yes (1.00): 52 (34.7%) | Total: 150

When asked whether services — both conventional and virtual — met the needs of young people, 65% responded "No." This result is consistent with the fifth null hypothesis: that young people are generally dissatisfied with available mental health services, which they feel are not structured to address their specific needs.

A linear regression was conducted to determine the extent to which each of six identified barriers predicted counselling services utilization rates among young people. Results are presented in Table 10.

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

(Constant): B = 2.818 | Std. Error = 0.457 | t = 6.171 | Sig. = 0.000

I will be perceived as weak and crazy: B = 0.057 | Std. Error = 0.049 | Beta = 0.104 | t = 1.164 | Sig. = 0.046

Lack information on when and where to seek care: B = −0.122 | Std. Error = 0.058 | Beta = −0.272 | t = −2.099 | Sig. = 0.038

I lack financial resources and insurance cover: B = −0.222 | Std. Error = 0.051 | Beta = −0.326 | t = −4.333 | Sig. = 0.000

Fear of losing my child's custody: B = −0.033 | Std. Error = 0.041 | Beta = −0.075 | t = −0.804 | Sig. = 0.423

Preference for alternative treatments: B = 0.000 | Std. Error = 0.064 | Beta = 0.000 | t = 0.002 | Sig. = 0.023

Fear of ruining employment prospects: B = −0.229 | Std. Error = 0.049 | Beta = −0.393 | t = −4.648 | Sig. = 0.000

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: fear of being perceived as weak or crazy; lack of information on when and where to seek help; lack of insurance and financial resources; preference for alternative treatments such as meditation; and fear that a mental illness record would damage employment prospects. For these five variables, the null hypothesis is rejected and the alternative hypothesis is accepted. The fear of losing child custody yielded insignificant results; the null hypothesis for this variable is therefore accepted. Based on the beta coefficients, lack of information on where and when to seek care, lack of financial resources and insurance coverage, and fear of ruining employment prospects exert the greatest influence on mental health services utilization.

Table 11. Model Summary

R = 0.570 | R² = 0.325 | Adjusted R² = 0.295 | Std. Error of the Estimate = 0.31139

The relatively low R-squared value indicates that the identified six factors — along with other significant factors not captured in this model — jointly predict the likelihood of utilizing mental health services. The aim of this study, however, is not to build a predictive model but to determine the individual effect of each barrier on utilization rates. Future studies could replicate this analysis with additional independent variables to improve the model's goodness of fit.