Sage Journals: Discover world-class research

Abstract

Depression and anxiety are prevalent mental health challenges experienced by youth and young adults; however, existing psychosocial interventions are not sufficiently effective. A growing body of research has examined the multiple factors that impact psychosocial treatment participation for youth with anxiety and depression. This scoping review uses the Health Belief Model (HBM) as a guiding framework to synthesize and categorize the factors that limit treatment participation (i.e., barriers) and those that are associated with greater treatment participation (i.e., facilitators) for youth aged 12–25 presenting to outpatient services for mood and anxiety difficulties. Abstracts and titles were reviewed for 5483 studies with 21 studies fitting full inclusion criteria. Most of the extracted factors fell within established HBM domains, with factors related to perceived barriers and severity most frequently reported. Relatively understudied areas included cues to action, perceived susceptibility, and self-efficacy. Also identified were multiple factors that serve as both barriers and facilitators depending on the context. The factors not captured by the HBM were socio-demographic factors and factors related to mental health service structure. Overall, this review aims to inform development of refined assessment and treatment approaches for youth with anxiety and/or depression at risk for early treatment termination.

Plain Language Summary

A Review of Studies to Identify Barriers and Facilitators of Psychosocial Treatment Participation for Youth with Depression and Anxiety: Depression and anxiety are prevalent mental health challenges experienced by youth and young adults; however, existing interventions are not sufficiently effective. A growing body of research has examined the multiple factors that impact intervention participation for youth with anxiety and depression. This study will review the literature to synthesize and categorize the factors that limit treatment participation (i.e., barriers) and those that are associated with greater treatment participation (i.e., facilitators) for youth aged 12-25 presenting to outpatient services for mood and anxiety difficulties. Abstracts and titles were reviewed for 5483 studies with 21 studies fitting full inclusion criteria. Factors related to perceived barriers and severity most frequently reported and multiple factors that serve as both barriers and facilitators depending on the context. Overall, this review aims to inform development of refined assessment and treatment approaches for youth with anxiety and/or depression at risk for early treatment termination.

Keywords

youth mental health mental health treatment barriers to treatment facilitators of treatment depression anxiety

Introduction

Depression and anxiety are prevalent mental health problems, affecting 15–20% of adolescents and young adults with rates substantially increasing in recent decades (Duffy et al., 2019; Shorey et al., 2022; Starcevic, 2006). Experiencing depression or anxiety as a youth or early adult can disrupt academic, social, and emotional development with long-term difficulties in education, employment, and interpersonal relationships (Chang & Kuhlman, 2022; Clayborne et al., 2019; Essau et al., 2014; Miles et al., 2018). A meta-analysis of 31 prospective studies pooling longitudinal data across multiple cohorts showed robust associations between depression in youth and later academic and vocational disadvantages (Clayborne et al., 2019). Further, depression onset in adolescence and early adulthood has been linked to lower household incomes and reduced likelihood of getting married and having children (Chang & Kuhlman, 2022). Similarly, experiencing anxiety in adolescence and early adulthood is associated with greater difficulties at age 30, including educational underachievement, interpersonal difficulties, chronic stress, substance abuse, and life and career dissatisfaction (Essau et al., 2014; Miles et al., 2018). Taken together, this body of research demonstrates that both depression anxiety in youth and early adulthood confer risk for poor outcomes (Chang & Kuhlman, 2022; Clayborne et al., 2019; Essau et al., 2014; Miles et al., 2018).

Psychotherapy is recommended as a first line intervention to treat mild to moderate anxiety and depression, with 70% of newly diagnosed youth choosing this option (Joyce et al., 2018). However, meta-analytic findings suggest that while psychosocial interventions can reduce depression and anxiety symptom severity, not all youth experience clinically significant benefits (Baker et al., 2021; Bear et al., 2020). Current evidence-based treatments for anxiety and depression have small to moderate effect sizes (Eckshtain et al., 2020; Fluckiger et al., 2014). Further, disorder heterogeneity and individual differences also make it challenging for evidence-based treatments to be universally applicable (Parker & Fletcher, 2007), suggesting a need to refine treatments to better reflect the unique needs of those failing to sufficiently benefit.

The efficacy or effectiveness of evidence-based treatments is often limited by low participation and early termination with only one-third of youth attending eight or more sessions and many evidence-based interventions requiring about 13–16 sessions (Fluckiger et al., 2020; Miller et al., 2008). Some research has sought to improve treatment participation by examining common barriers to care, including time limitations and other responsibilities (e.g., work, school, babysitting younger siblings; O'Connor et al., 2020; Meredith et al., 2009; Seligman et al., 2020), having comorbid mental health challenges (O’Dor et al., 2021), being a racial or ethnic minority (Miranda et al., 2015; Zarger & Rich, 2016), low socioeconomic status (Zarger & Rich, 2016), and skepticism around treatment effectiveness (Mehra et al., 2021; Miranda et al., 2015; Moore et al., 2020). These barriers are associated with lower treatment utilization, engagement, and adherence (O’Connor et al., 2020; Mehra et al., 2021; Meredith et al., 2009; Miranda et al., 2015; Moore et al., 2020; Seligman et al., 2020; Zarger & Rich, 2016) and less post-treatment symptom and impairment reduction (Abbott et al., 2019). Additionally, treatment facilitators commonly reported include perception of potential benefit, therapist factors (e.g., rapport, knowledge, collaboration), and treatment availability (O'Dor et al., 2021; Upadhyay et al., 2019; Wisdom et al., 2006)). However, to date, factors that influence treatment participation for youth with depression and anxiety have not been systematically reviewed. Given the variety and range of factors studied, categorizing and consolidating the evidence is an important next step in informing opportunities for treatment innovation and further research.

Health Belief Model

Psychosocial treatment barriers and facilitators may be understood within the Health Belief Model (HBM), which was initially developed to categorize treatment seeking behaviour for physical health conditions but has also been used in a mental health context (Nobiling & Maykrantz, 2017; Smith, 2009) and with a range of age groups, including adolescents and young adults (Rickwood et al., 2005). The HBM proposes six domains that influence treatment participation: perceived susceptibility, perceived severity, self-efficacy, perceived benefits, perceived barriers, and cues to action (Nobiling & Maykrantz, 2017).

Perceived susceptibility refers to how vulnerable an individual believes they are to a health problem and is predictive of help seeking intentions (Connor et al., 2014; Kim & Zane, 2016). Failing to recognize treatment need increases the time between symptom onset and disorder recognition, with research demonstrating an average delay of 8 years in diagnosing mood or anxiety disorders and an additional one-year delay to seek treatment (Thompson et al., 2008).

Perceived severity refers to how much an individual believes symptoms have serious functional consequences (Kim & Zane, 2015), and is a robust predictor of treatment seeking and engagement (Abbott et al., 2019; Lilly et al., 2020; O’Dor et al., 2021). In some contexts, less severe symptoms promote treatment engagement and benefit with more severe symptoms predicting lower treatment engagement and poorer clinical outcomes (e.g., O’Dor et al., 2021; Seligman et al., 2020). Further, symptom severity is associated with negative self-talk and hopelessness, which may reduce understanding and application of intervention components (Abbott et al., 2019; O'Dor et al., 2021). However, the literature is mixed, with other studies indicating that higher severity promotes engagement and improved clinical outcomes, possibly through better recognition of treatment necessity and increased motivation for change (e.g., Li et al., 2019; Wu et al., 2001).

The next component of the HBM is self-efficacy, which is the belief in one’s ability to promote change (Nobiling & Maykrantz, 2017). Some research has shown that higher self-efficacy is a treatment facilitator associated with greater treatment utilization and positive outcomes in young adults with depression (Lilly et al., 2020). However, findings are inconsistent, with lower self-efficacy at times facilitating treatment participation through its association with greater perceived symptom severity and uncertainty regarding independent symptom management (Andersson et al., 2014; Keeling et al., 2020).

Perceived benefits refer to a person’s belief in the effectiveness of the treatment and its potential to reduce distress (Kim & Zane, 2016; Lilly et al., 2020). Benefits may include expected improvements in emotional functioning, coping with stress, processing past experiences, handling current stress, and reducing problematic thinking and behaviours (Mehra et al., 2021). In contrast, skepticism suggests low perceived benefit and is a barrier to engagement (Mehra et al., 2021; Meredith et al., 2009; Miranda et al., 2015; Moore et al., 2020; Stafford & Draucker, 2020). Of note, some research indicates that racial minorities perceive fewer psychosocial treatment benefits, which can worsen treatment participation within marginalized communities (Kim & Zane, 2016).

Perceived barriers are a next domain of the HBM and robust cross-cultural predictors of treatment participation in anxious adolescents and young adults (Kim & Zane, 2016; O’Connor et al., 2020). Many youth perceive barriers to attending treatment such as time constraints, transportation challenges, and financial limitations (Mehra et al., 2021; Moore et al., 2020). These barriers have a particularly adverse effect on treatment outcomes for at-risk populations (Golberstein et al., 2019; Miranda et al., 2015; Zarger & Rich, 2016).

Finally, cues to action refer to proximal drivers of treatment participation that increase motivation to participate in an intervention (Dillon et al., 2020). Cues to action may be internal (e.g., thoughts about treatment, realization of problem severity) or external (e.g., advertisement, recent referral to treatment program, interpersonal experiences, personal crises) (Castonguay et al., 2016; Daud et al., 2020; Lilly et al., 2020; Nobiling & Maykrantz, 2017).

Current Study

The objective of this scoping review is to synthesize and categorize findings from empirical studies that have identified barriers and facilitators that influence psychosocial treatment participation in youth aged 12–25 presenting to outpatient mental health services for mood and anxiety difficulties. Barriers and facilitators will be described, categorized, and charted within the HBM framework adapted for mental health. Factors that do not fit within the original HBM will be analyzed thematically as possible extensions of the HBM. As the HBM was initially developed to understand medical treatment seeking behaviour, a review of the mental health literature may reveal important gaps in the HBM with proposed extensions to better capture help-seeking behaviour in mental health contexts. Additionally, this study is expected to increase understanding of common barriers and facilitators to mental health care, ultimately generating knowledge that may inform future studies and potential modifications for psychosocial treatment for youth with depression or anxiety.

Methodology

Study methodology was informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) (Tricco et al., 2018). Articles published in print or online from January 2000 to May 2022 were identified through systematic searches in Medline (Ovid), Embase (Ovid), APA PsycInfo (Ovid), and CINAHL (EBSCO). The search strategy was first drafted in Medline by a health sciences librarian (TR) and refined through team discussions. The final search strategy (Supplemental Table 1) was then translated and executed in the four selected databases on May 27, 2022. All strategies used database-specific subject headings, keywords in natural language, and advanced search operators to capture and combine multiple concepts. This multi-stranded approach was needed to identify articles that might meet the complex set of eligibility criteria, which included individual factors, treatment factors, participation, and outcomes. Final search results were imported into online review software Covidence, which automatically removed duplicates. Year limit was 2000 to date of search. No language or study type limits were applied. Conference abstracts and dissertations were removed when possible.

Inclusion criteria were: (1) participants aged 12–25 and/or their parents/caregivers; (2) youth presenting for and/or engaging in psychosocial treatments for a mood and/or anxiety disorder; (3) inclusion of a measure and/or report of a barrier and/or facilitator associated with psychosocial treatment participation and/or outcomes; (4) English publication; (5) peer-reviewed publication; (6) outpatient psychosocial treatment; and (7) North American samples. The decision to restrict to North American samples ensured a focused synthesis with contextual comparability across health systems and cultural frameworks, whereas including global literature would have introduced substantial variability that could obscure patterns relevant to clinical practice in North America.

Exclusion criteria were: (1) the majority of study participants fell outside the age range of 12–25 years; (2) participants seeking or in treatment for autism, schizophrenia, psychotic disorders, or other mental disorders; (3) study did not include a measure and/or report of a barrier and/or facilitator; (4) published in languages other than English; (5) non-peer-reviewed publications (e.g., grey literature, theses), review papers, commentaries, case studies, or study protocols; (6) did not include outpatient treatment; (7) participants resided outside of North America. Articles were included if most data met inclusion requirements (e.g., if the majority of sample fell within the age range) or if exclusion conditions were analyzed additionally or separately (e.g., separate analyses for psychopharmacological and psychotherapy treatment conditions).

Two reviewers (ADS and EN) independently screened titles and abstracts for primary inclusion. Conflicts were resolved in team discussions with senior co-authors (ACB, JK, BA). Full texts of eligible articles were obtained and rescreened by the two reviewers (ADS, EN). Conflicts were again resolved through team discussions with senior co-authors (ACB, JK, BA). A data extraction form was created and included fields for the following: basic article information (e.g., authors, publication year, study location); participant demographics (e.g., age, gender, socioeconomic status, ethnicity); informant (e.g., parent or youth); sample size; study design; disorder type; psychosocial intervention type; treatment setting; timing of data collection (e.g., before, during, or after treatment); types of barriers and/or facilitators; and relevant measures of identified barriers and/or facilitators. Two reviewers (ADS and EN) independently charted the data.

The raw data derived from the extraction form was reviewed by co-authors and reduced to reflect the most relevant information organized alphabetically by author. All identified barriers and facilitators were then categorized according to the domains of the HBM. Treatment barriers and facilitators that best fit proposed additions of the HBM specific for mental health (e.g., service structure factors, socio-demographic factors) were also included.

Results

Study Selection

The PRISMA flow diagram (Figure 1) outlines the number of articles included and excluded at each stage of the review, resulting in a final total of 21 articles that were included.

Figure 1.

PRISMA flow diagram

Study characteristics and key findings related to treatment barriers and facilitators can be found in Supplemental Table 2. Included articles used American (n = 16) and Canadian (n = 5) samples with individuals with depression (n = 12), anxiety (n = 5), or both (n = 4). Sample sizes ranged from N = 22 to N = 9896, and the majority of samples were predominantly white (67%) and female (81%). Most studies employed quantitative analyses (n = 15); others used mixed methods (n = 4) or qualitative (n = 2) approaches.

Study Categorization

Reported barriers and facilitators were categorized within the six domains of the HBM. Perceived facilitators were considered alongside perceived barriers. Two additional categories were added to capture barriers and facilitators related to service structure factors and socio-demographic factors, which are important in mental healthcare and not captured within existing categories of the HBM (Nobiling & Maykrantz, 2017). Tables 1 and 2 categorize identified barriers and facilitators respectively within this extended HBM.

Table 1.

Psychosocial Treatment Barriers in Youth With Symptoms of Depression and Anxiety

Perceived susceptibility
Uncertainty about treatment need	Mehra et al. (2021), Miranda et al. (2015), Moore et al. (2020), Stafford & Draucker (2020)
Perceived severity
More severe mental health problem(s)	Abbott et al. (2019), Miranda et al. (2015), O'Dor et al. (2021), Seligman et al. (2020)
Less severe mental health problem(s)	Osuch et al. (2019)
Longer symptom duration	O'Dor et al. (2021)
Comorbidities	O'Dor et al. (2021)
Symptom type (e.g., primarily somatic symptoms)	Schuler et al. (2021)
Comorbid substance use	Osuch et al. (2019)
Self-efficacy
Preferring to seek help from friends/family	Miranda et al. (2015)
Wanting to handle problems on their own	Mehra et al. (2021), Miranda et al. (2015), Moore et al. (2020)
High expectations of self	Wisdom et al. (2006)
Perceived benefits
Skepticism about treatment effectiveness	Mehra et al. (2021), Meredith et al. (2009), Miranda et al. (2015), Moore et al. (2020), Stafford & Draucker (2020)
Perceived barriers
Not knowing who to see	Mehra et al. (2021)
Inconvenient, time-consuming, schedule difficulties, other responsibilities	Mehra et al. (2021), Miranda et al. (2015), Moore et al. (2020), O'Connor et al. (2020), Seligman et al. (2020)
Financial concerns	Mehra et al. (2021), Miranda et al. (2015), Moore et al. (2020), Seligman et al. (2020), Stafford & Draucker (2020)
Transportation or travel distance	Mehra et al. (2021), Moore et al. (2020), Upadhyay et al. (2019)
Fear of involuntary hospitalization	Mehra et al. (2021), Stafford & Draucker (2020)
Belief that psychopathology indicates weakness	Stafford & Draucker (2020), Wisdom et al. (2006)
Concerns about confidentiality	Wisdom et al. (2006)
Cues to action
None reported
Service structure factors
Stigma or shame	Mehra et al. (2021), Meredith et al. (2009), Miranda et al. (2015), Moore et al. (2020), Wisdom et al. (2006)
Access (e.g., knowledge of services, appointment availability)	Mehra et al. (2021), Miranda et al. (2015), Moore et al. (2020), Stafford & Draucker (2020)
Discomfort with professional	Miranda et al. (2015)
Previous negative experience with services	Mehra et al. (2021), Miranda et al. (2015), Stafford & Draucker (2020)
Confidentiality concerns	Wisdom et al. (2006)
Limited understanding of problem or treatment	Wisdom et al. (2006)
Socio-demographic factors
Higher family support	LeCloux et al. (2016)
Lower family support	Meredith et al. (2009), Miranda et al. (2015), Moore et al. (2020), Seligman et al. (2020)
Cultural/religious beliefs of family	Moore et al. (2020)
Greater maternal life stress	Wilansky-Traynor et al. (2010)
Being a racial/ethnic minority	Miranda et al., 2015, Wu et al. (2001), Zarger & Rich (2016)
Poverty	Zarger & Rich (2016)

Table 2.

Psychosocial Treatment Facilitators in Youth With Symptoms of Depression and Anxiety

Perceived Susceptibility
None reported
Perceived severity
More severe mental health problem(s)	LeCloux et al. (2016), Li et al. (2019), Schuler et al. (2021), Wilansky-Traynor et al. (2010), Wu et al. (2001)
Less severe mental health problem(s)	Abbott et al. (2019), O'Dor et al. (2021)
Shorter symptom duration	O'Dor et al. (2021)
More comorbidities	Abel et al. (2022), Zarger & Rich (2016)
Less or no comorbidities	Lee et al. (2019), O'Dor et al. (2021)
Self-efficacy
Feeling responsible for own well-being	Moore et al. (2020)
New perspective and skills gained in therapy	Stafford & Draucker (2020)
Perceived benefits
Parent pre-treatment expectations and perceptions	Lee et al. (2019), Wu et al. (2001)
Belief that therapy will help	Mehra et al. (2021), O'Dor et al. (2021)
Perceived facilitators
Therapist knowledge of client circumstances	Moore et al. (2020), Wisdom et al. (2006)
Therapist rapport and perception of investment	Moore et al. (2020), Stafford & Draucker (2020), Wisdom et al. (2006)
Therapists with marginalized identities	Moore et al. (2020)
Therapist collaboratively plans treatment with client	Wisdom et al. (2006)
Discussing interplay between symptoms and societal discrimination	Moore et al. (2020)
Cues to action
Suicide attempt	LeCloux et al. (2016)
Service structure factors
Finding low-cost services	Moore et al. (2020)
Ease of program use and understanding	O'Connor et al. (2020)
Internet access	O'Connor et al. (2020)
Greater provider availability	Upadhyay et al. (2019)
Better first session therapeutic relationship	Lee et al. (2019)
Longer treatment length	Li et al. (2019)
Simultaneous pharmacological intervention	O'Dor et al. (2021)
Seeing mental health specialist as opposed to a general practitioner	Upadhyay et al. (2019)
Use of psychoeducation	Wisdom et al. (2006)
Understanding privacy/confidentiality	Wisdom et al. (2006)
Socio-demographic factors
High family support	Moore et al. (2020), Stafford & Draucker (2020)
Low family support	LeCloux et al. (2016)
Support with finding services	Moore et al. (2020)
Living with both parents	Lee et al. (2019)
Less parental psychopathology	Lee et al. (2019), Wilansky-Traynor et al. (2010)
Less family dysfunction	Lee et al. (2019)
Higher family income and SES	Lee et al. (2019), O'Dor et al. (2021)
Maternal treatment utilization	Sherman & Ali (2017), Wu et al. (2001)
Younger age	Abbott et al. (2019), Wilansky-Traynor et al. (2010)
Transitioning to adulthood	Moore et al. (2020)
Greater parent education	Wilansky-Traynor et al. (2010)
Being non-racialized	Mehra et al. (2021)
Being female	Mehra et al. (2021)
Having 1+ caregivers born in Canada	Mehra et al. (2021)

Study Findings within the HBM Framework

Uncertainty about treatment need was a treatment barrier related to the perception of susceptibility and was reported in four studies (Mehra et al., 2021; Miranda et al., 2015; Moore et al., 2020; Stafford & Draucker, 2020). No facilitators related to perceived susceptibility were identified.

Mental health problem severity findings were mixed, with greater severity being both a barrier (Abbott et al., 2019; Miranda et al., 2015; O'Dor et al., 2021) and a facilitator (LeCloux et al., 2016; Li et al., 2019; Schuler et al., 2021; Wilansky-Traynor et al., 2010; Wu et al., 2001). Interestingly, lower severity of mental health problems was also both a barrier (Osuch et al., 2019) and a facilitator (Abbott et al., 2019; O'Dor et al., 2021). Findings were also mixed with comorbidities being both a treatment barrier (Lee et al., 2019; O'Dor et al., 2021; Osuch et al., 2019) and a facilitator (Abel et al., 2022; Zarger & Rich, 2016). Symptom duration findings showed longer duration being a barrier and shorter duration being a facilitator of better treatment outcomes; however, this was found in just one study (O'Dor et al., 2021).

Higher self-efficacy was a treatment facilitator as indexed by feeling responsible for one’s own well-being and developing new skills and perspectives over the course of therapy (Moore et al., 2020; Stafford & Draucker, 2020). However, when individuals had high expectations of self and preferred to handle challenges independently or with social support, this type of increased self-efficacy was reported as being a treatment barrier (Mehra et al., 2021; Miranda et al., 2015; Moore et al., 2020; Wisdom et al., 2006). When parents had higher pre-treatment expectations, and clients believed therapy would help, participation was greater (Lee et al., 2019; Mehra et al., 2021; O'Dor et al., 2021; Wu et al., 2001). Conversely, when skepticism was high and treatment benefits were unclear, there was a greater likelihood of treatment disengagement and drop-out (Mehra et al., 2021; Meredith et al., 2009; Miranda et al., 2015; Moore et al., 2020; Stafford & Draucker, 2020).

There were many perceived logistical barriers to treatment participation reported by parents and youth, including not knowing who to see, time constraints, financial concerns, transportation challenges, and other responsibilities (Mehra et al., 2021; Miranda et al., 2015; Moore et al., 2020; O'Connor et al., 2020; Seligman et al., 2020; Stafford & Draucker, 2020). Also reported, and maybe somewhat unique to mental health care, were concerns about confidentiality, the possibility of involuntary hospitalization, and whether psychopathology indicates weakness (Mehra et al., 2021; Stafford & Draucker, 2020; Wisdom et al., 2006).

On the other hand, studies also reported several perceived facilitators of treatment participation including therapist knowledge of circumstances, therapist-client rapport, client perception of therapist’s investment in their care, and degree of collaborative treatment planning (Moore et al., 2020; Stafford & Draucker, 2020; Wisdom et al., 2006). Further, in one study clients with marginalized identities, such as racial or sexual minorities, engaged better with therapists with similar identities (Moore et al., 2020).

Cues to action are proximal precipitants of treatment seeking behaviour, such as suicidal behaviour prompting an individual to engage in treatment to reduce distressing and impairing symptoms (LeCloux et al., 2016). Cues to action were less relevant as treatment barriers, with none reported in the reviewed studies. Attempted suicide was a cue to action that facilitated treatment-seeking behaviour (LeCloux et al., 2016).

Emergent Domains Beyond the HBM

A service structure factors category was created, because factors specific to mental health service use were not adequately captured by the HBM. Barriers to treatment classified within this category include stigma or shame (Mehra et al., 2021; Meredith et al., 2009; Miranda et al., 2015; Moore et al., 2020; Wisdom et al., 2006), client discomfort after poor treatment experiences (Mehra et al., 2021; Miranda et al., 2015; Stafford & Draucker, 2020), and clients not understanding their symptoms and treatment options (Wisdom et al., 2006). There were also facilitators related to service structure such as the use of psychoeducation, collaborative treatment planning, and explanations of confidentiality (O'Connor et al., 2020; Wisdom et al., 2006). Clients also participated more when services were low cost (Moore et al., 2020), there was greater provider availability, and the provider was a mental health specialist as opposed to a general practitioner (Upadhyay et al., 2019). When treatments were virtual, other factors such as internet access became important (O’Connor et al., 2020). Treatment-related factors (e.g., early therapeutic rapport, longer treatment length, simultaneous pharmacological treatment) also facilitated treatment participation (Lee et al., 2019; Li et al., 2019; O'Dor et al., 2021).

The reviewed studies identified several socio-demographic factors related to treatment participation. Findings were mixed with regards to family support, with one study reporting it as a barrier (LeCloux et al., 2016) Formatting… and five studies identifying it as a facilitator (Meredith et al., 2009; Miranda et al., 2015; Moore et al., 2020; Seligman et al., 2020; Stafford & Draucker, 2020). Other family factors such as family cultural and religious beliefs, worse family functioning, and parental psychopathology also predicted less treatment participation (Lee et al., 2019; Moore et al., 2020; Wilansky-Traynor et al., 2010). Further, two studies found that when mothers had previous therapeutic experiences, their adolescents were more likely to participate in treatment (Sherman & Ali, 2017; Wu et al., 2001).

Demographic barriers to treatment participation included being racialized or low-income (Lee et al., 2019; Miranda et al., 2015; O'Dor et al., 2021; Wilansky-Traynor et al., 2010; Wu et al., 2001; Zarger & Rich, 2016). Demographic facilitators included younger age (Abbott et al., 2019; Wilansky-Traynor et al., 2010); emerging adulthood (Moore et al., 2020), greater parent education (Wilansky-Traynor et al., 2010), being female (Mehra et al., 2021), living with both parents (Lee et al., 2019), and having multiple caregivers born in Canada (Mehra et al., 2021).

Discussion

This study aimed to systematically review studies that identified barriers and facilitators related to psychosocial treatment participation for youth aged 12–25 with depression and/or anxiety. Identified barriers and facilitators were then classified within the domains of the HBM, which has been widely used to understand participation in healthcare (Nobiling & Maykrantz, 2017). Although this model captured the majority of identified psychosocial treatment barriers and facilitators, two categories were added that may be key within mental health contexts: service structure and socio-demographic factors. Overall, the most studied factors fell within the domains of perceived severity, perceived barriers, service structure factors, and socio-demographic factors. Relatively understudied domains included cues to action, perceived susceptibility, and self-efficacy. There were also mixed findings within many domains, with multiple factors that serve as either barriers or facilitators depending on the context.

Perceived severity was a highly studied domain of the HBM with inconsistent findings; it was identified as both a barrier and a facilitator of treatment participation (Abbott et al., 2019; LeCloux et al., 2016; Li et al., 2019; Miranda et al., 2015; O'Dor et al., 2021; Osuch et al., 2019; Schuler et al., 2021; Wilansky-Traynor et al., 2010; Wu et al., 2001). For example, in two studies high anxiety severity was associated with increased avoidance of anxiety-provoking stimuli encountered in aversion to the therapeutic process, and reduced participation in treatment (Lehrbach et al., 2023; Lorian & Grisham, 2011). In contrast, other studies identified higher severity and longer symptom duration as motivators (facilitators) of treatment participation (Buckman et al., 2021; Klein et al., 2011). One explanation was that better treatment response may be achieved in cases of higher severity because there is more room for improvement compared to individuals with lower severity (Elices et al., 2022). Indeed, youth with more complex concerns, greater severity of psychopathology, and comorbid diagnoses may require more intensive and long-term treatment (Delgadillo et al., 2017).

It appears that the role of severity as a treatment barrier or facilitator was dependent on how it was operationalized, with some research suggesting that severity defined in terms of functional impairments was more limiting than severity indicated by increased symptomatology although additional research is needed in this area (O'Dor et al., 2021). In one study, higher initial severity was both a barrier associated with less symptom improvement and a facilitator associated with more rapid change, depending on how outcomes were defined (Stochl et al., 2022). Overall, although a preponderance of research has examined the role of perceived severity in treatment participation, findings remain mixed, and further research is needed to better understand how severity-related predictors interact to influence treatment participation in youth and young adults with depression and anxiety.

Another domain of the HBM that was highly researched was perceived barriers, which included logistical barriers (e.g., time constraints, transportation challenges) and other beliefs (e.g., involuntary hospitalization, being seen as weak); this was consistent with prior research showing that perceived barriers predicted treatment participation over and above demographic factors, symptom severity, and stressful life events (Goetter et al., 2020; Kazdin et al., 1997). The most identified facilitators of treatment participation were factors related to the therapist or the therapeutic process, such as a strong therapist-client bond (i.e., therapeutic alliance) and collaborative goal setting (Moore et al., 2020; Stafford & Draucker, 2020; Wisdom et al., 2006). These factors were identified repeatedly across the reviewed studies, suggesting that they may be important during the course of treatment. However, their relative importance or impact on client treatment participation has not yet been studied systematically.

Within the HBM, few studies identified cues to action, which are proximal treatment-facilitating factors that drive service seeking behaviours (Nobiling & Maykrantz, 2017). Cues to action were relatively understudied predictors of treatment participation (LeCloux et al., 2016; Monti et al., 2003). Suicidal behaviour emerged as an important cue to action in depressed adolescents (LeCloux et al., 2016), which was consistent with other research findings indicating that 68% of suicide attempters were in treatment one-month post-attempt and 62% at three months post-attempt (Monti et al., 2003). Suicidal behaviour can be a signal that urgent intervention is needed (LeCloux et al., 2016; Lizardi & Stanley, 2010). However, more research is needed to elucidate additional cues to action that predict participation in youth with mood and anxiety difficulties. These may include factors such as physician referrals or targeted advertisements (Nobiling & Maykrantz, 2017).

Perceived susceptibility, a pre-treatment mindset regarding a disorder, is believed to impact treatment participation (Nobiling & Maykrantz, 2017). Within the reviewed literature, only one barrier fit within the perceived susceptibility domain: uncertainty about treatment need (Mehra et al., 2021; Miranda et al., 2015; Moore et al., 2020; Stafford & Draucker, 2020). No facilitators were classified within the perceived susceptibility domain. However, four of the twenty-one reviewed studies (19%) identified uncertainty about treatment need as an important factor that is frequently reported in youth seeking treatment for mood and anxiety disorders.

Barriers and facilitators related to self-efficacy were also relatively understudied with inconsistent findings. Theories of motivation have highlighted the importance of self-efficacy, positing that individuals put forth more effort when they believe in their ability to achieve goals (Eccles & Wigfield, 2002). Bandura (1997) suggested that approaching and coping with previously avoided stimuli increases self-efficacy, with such strategies often employed in interventions such as Cognitive Behavioural Therapy (CBT). Subsequent research has extended this finding, demonstrating that self-efficacy improves with other treatment modalities (e.g., Acceptance and Commitment Therapy, Dialectical Behaviour Therapy) and with a variety of anxiety disorders (e.g., panic disorder, post-traumatic stress disorder, social anxiety disorder) and other mental health challenges (e.g., eating disorders) (Brown et al., 2014; Keshen et al., 2017). Similarly, in a sample of individuals with Generalized Anxiety Disorder, as CBT progressed, self-efficacy improved, and severity of anxious and depressive symptoms decreased (Visla et al., 2023). Self-efficacy was reported as a treatment facilitator with individuals developing new skills and positive perspectives over the course of therapy (Stafford & Draucker, 2020). However, high self-efficacy was a barrier when high expectations led to individuals wanting to solve problems independently or with family support rather than seek formalized treatment (Mehra et al., 2021; Miranda et al., 2015; Moore et al., 2020; Wisdom et al., 2006). Further, when self-efficacy was higher, it resulted in unrealistic treatment expectations (Visla et al., 2023) or increased worrying and negative emotionality as participants “prepared” themselves to face challenges (Newman & Llera, 2011; Visla et al., 2023). This could suggest a nonlinear association or a “Goldilocks effect” in which a moderate degree of self-efficacy might be most beneficial for optimal treatment participation (McCarthy et al., 2016)(). The influence of self-efficacy on treatment participation requires further nuanced study to unpack when and how it facilitates or negatively influences treatment participation.

While the majority of identified treatment barriers and facilitators fit within the HBM framework, two domains were created to capture additional treatment barriers and facilitators that were identified: sociodemographic factors and factors related to mental health service structure. Identified service structure barriers included youth perceptions of stigma and shame, which may be particularly relevant in mental health disorders and treatments (Wisdom et al., 2006). The reviewed articles also identified therapeutic alliance as a service structure facilitator, which was consistent with research linking therapeutic alliance to better treatment outcomes when compared to demographic factors, pre-treatment psychopathology, treatment modality, and setting (Barnicot et al., 2011; Steuwe et al., 2023). Logistical service structure factors identified in this review (e.g., costs, time constraints, provider availability, other responsibilities) also aligned with those reported in other research studies (Goetter et al., 2020; Kazdin et al., 1997).

Although demographic factors were reported as treatment barriers or facilitators in many studies, these findings should be interpreted with caution as meta-analytic findings have shown no robust demographic predictors of treatment participation (Barnicot et al., 2011; Cougle & Grubaugh, 2022). More research is needed to examine the interplay between racial factors, gender and sexuality factors, and social determinants of health (Barnicot et al., 2011; Cougle & Grubaugh, 2022). The Contact Hypothesis offers one possible explanation, as the treatment-seeking decisions of youth are likely influenced by relational dynamics such as trust and equity (Kennedy et al., 2021). Future research could test interventions that operationalize this theory, such as pairing youth with culturally competent providers or implementing structured trust-building strategies early in treatment.

Social support emerged as another predictor of treatment participation, with some studies suggesting higher support was better (Meredith et al., 2009; Miranda et al., 2015; Moore et al., 2020; Seligman et al., 2020; Stafford & Draucker, 2020) and one study suggesting that lower support facilitated participation (LeCloux et al., 2016). Two models might explain this discrepancy: the “deficit model” suggests that psychotherapy is utilized more when external support is limited, and the “competence model” posits that greater social support reflects well-developed skills needed to participate in treatment (Herrle & Angermeyer, 1997). More recent research has reasoned that lower support predicts increased psychosocial treatment utilization as some seek treatment to improve social relationships and family functioning (Graziano & Elbogen, 2017; Jaffe et al., 2023). Due to these complexities, the role of social support as a treatment barrier or facilitator should be individually assessed during intake or early in treatment.

Limitations

This study is limited in that the reviewed articles utilized a variety of incomparable methodologies, and many had insufficient data to support quantitative analyses. Further, study design rigor was variable and often dictated by feasibility within the research setting. However, including articles with a range of methodologies provided better ecological validity with data representative of real-world mental health contexts. Another challenge of this study was establishing search terms as there is no clearly established terminology to define the constructs of interest. For instance, treatment participation may be described in terms of attendance, engagement/disengagement, drop out, retention, attrition, discontinuation, or early termination. To manage this challenge, a research librarian (TR) developed and refined the search strategy (Supplemental Table 1) through an iterative process with senior co-authors until the search strategy was determined to capture the most relevant articles.

Another limitation was that most studies included a predominantly white and female sample, with racially, ethnically, and gender-diverse populations underrepresented in included studies. Additionally, this review examined only North American research in order to maintain feasibility and maximize contextual comparability across studies, as initial searches indicated that global literature produced a large body of research with substantial variation in health systems and cultural frameworks. Although restricting to North American research allowed for a more focused synthesis, there is a need for future research to include diverse samples and examine whether findings are replicable cross-culturally.

Conclusions

Overall, this review highlights that deciding to engage in psychosocial treatment is a complex process influenced by several factors across a variety of domains. This may guide clinical decision-making through refined assessment approaches and treatment adaptations that consider barriers to and facilitators of mental health care. This review also provides insight into both the relatively understudied factors that impact treatment participation and the factors that have been examined more frequently. Ultimately, this review hopes to spur subsequent research to inform tailored service provision for youth with anxiety and depression.

Supplemental Material

Supplemental Material - Barriers and Facilitators of Psychosocial Treatment Participation for Youth With Depression and Anxiety: A Scoping Review

Supplemental Material for Barriers and Facilitators of Psychosocial Treatment Participation for Youth With Depression and Anxiety: A Scoping Review by Anneesa D. Singh, Anne-Claude V. Bedard, Terri Rodak, Elida Nexha, John Krzeczkowski, André Plamondon, and Brendan F. Andrade in Clinical Child Psychology and Psychiatry

Footnotes

ORCID iD

Brendan F. Andrade

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ontario Graduate Scholarship.

Declaration of Conflicting Interests

The authors report there are no competing interests to declare.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Anneesa Singh, MA, is a graduate student at the Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and the Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health and the Ontario Institute for Studies in Education at the University of Toronto, Toronto, Ontario, Canada.

Anne-Claude V. Bedard, PhD, is an Assistant Professor, Teaching Stream at the Ontario Institute for Studies in Education at the University of Toronto, Toronto, Ontario, Canada.

Terri Rodak, MISt, is a librarian at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Elida Nexha, HBSc, was a research assistant at the Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

John Krzeczkowski, PhD, is an Assistant Professor in the Department of Health Sciences at Brock University, St. Catherine’s, Ontario, Canada.

André Plamondon, PhD, is a Professor in the Department of Educational Fundamentals and Practices at Laval University, Quebec City, Quebec, Canada.

Brendan F. Andrade, PhD, is a senior scientist with the Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health at the Centre for Addiction and Mental Health and Assistant Professor in the Department of Psychiatry at the University of Toronto, Toronto, Ontario, Canada.

References

Abbott

C. H.

Zisk

Bounoua

Diamond

G. S.

Kobak

(2019). Predicting patterns of treatment response and outcome for adolescents who are suicidal and depressed. Journal of the American Academy of Child & Adolescent Psychiatry, 58(9), 897–906. https://doi.org/10.1016/j.jaac.2018.12.013

Abel

M. R.

Bianco

Gilbert

Schleider

J. L.

(2022). When is psychotherapy brief? Considering sociodemographic factors, problem complexity, and problem type in U.S. adolescents. Journal of Clinical Child and Adolescent Psychology, 51(5), 740–749. https://doi.org/10.1080/15374416.2020.1759076

Andersson

L. M. C.

Moore

C. D.

Hensing

Krantz

Staland-Nyman

(2014). General self-efficacy and its relationship to self-reported mental illness and barriers to care: A general population study. Community Mental Health Journal, 50(6), 721–728. https://doi.org/10.1007/s10597-014-9722-y

Baker

H. J.

Lawrence

P. J.

Karalus

Creswell

Waite

(2021). The effectiveness of psychological therapies for anxiety disorders in adolescents: A meta-analysis. Clinical Child and Family Psychology Review, 24(4), 765–782. https://doi.org/10.1007/s10567-021-00364-2

Bandura

(1997). Self efficacy: The exercise of control. W. H. Freeman and Company.

Barnicot

Katsakou

Marougka

Priebe

(2011). Treatment completion in psychotherapy for borderline personality disorder: A systematic review and meta-analysis. Acta Psychiatrica Scandinavica, 123(5), 327–338. https://doi.org/10.1111/j.1600-0447.2010.01652.x

Bear

H. A.

Edbrooke-Childs

Norton

Krause

K. R.

Wolpert

(2020). Systematic review and meta-analysis: Outcomes of routine specialist mental health care for young people with depression and/or anxiety. Journal of the American Academy of Child & Adolescent Psychiatry, 59(7), 810–841. https://doi.org/10.1016/j.jaac.2019.12.002

Brown

L. A.

Wiley

J. F.

Wolitzky-Taylor

Roy-Byrne

Sherbourne

Stein

M. B.

Sullivan

Rose

R. D.

Bystritsky

Craske

M. G.

(2014). Changes in self-efficacy and outcome expectancy as predictors of anxiety outcomes from the CALM study. Depression and Anxiety, 31(8), 678–689. https://doi.org/10.1002/da.22256

Buckman

J. E. J.

Saunders

Cohen

Z. D.

Barnett

Clarke

Ambler

DeRubeis

R. J.

Gilbody

Hollon

S. D.

Kendrick

Watkins

Wiles

Kessler

Richards

Sharp

Brabyn

Littlewood

Salisbury

White

I. R.

Pilling

(2021). The contribution of depressive ‘disorder characteristics’ to determinations of prognosis for adults with depression: An individual patient data meta-analysis. Psychological Medicine, 51(7), 1068–1081. https://doi.org/10.1017/S0033291721001367

10.

Castonguay

Filer

C. R.

Pitts

M. J.

(2016). Seeking help for depression: Applying the health belief model to illness narratives. Southern Communication Journal, 81(5), 289–303. https://doi.org/10.1080/1041794X.2016.1165729

11.

Chang

Kuhlman

K. R.

(2022). Adolescent-onset depression is associated with altered social functioning into middle adulthood. Scientific Reports, 12(1), 17320. https://doi.org/10.1038/s41598-022-22131-1

12.

Clayborne

Z. M.

Varin

Colman

(2019). Systematic review and meta-analysis: Adolescent depression and long-term psychosocial outcomes. Journal of the American Academy of Child & Adolescent Psychiatry, 58(1), 72–79. https://doi.org/10.1016/j.jaac.2018.07.896

13.

Connor

P. J.

Martin

Weeks

C. S.

Ong

(2014). Factors that influence young people's mental health help-seeking behaviour: A study based on the health belief model. Journal of Advanced Nursing, 70(11), 2577–2587. https://doi.org/10.1111/jan.12423

14.

Cougle

J. R.

Grubaugh

A. L.

(2022). Do psychosocial treatment outcomes vary by race or ethnicity? A review of meta-analyses. Clinical Psychology Review, 96(102192). https://doi.org/10.1016/j.cpr.2022.102192

15.

Daud

Hanafi

W. N. W.

Abdullah

W. M. T. W.

Ahmad

N. N.

Toolib

S. N.

(2020). Applying health belief model in assessing Malaysian graduate's emotional wellness post COVID 19 outbreak: A conceptual paper. Global Business and Management Research, 12(4), 580–588. https://search.proquest.com/docview/2826430451

16.

Delgadillo

Huey

Bennett

McMillan

(2017). Case complexity as a guide for psychological treatment selection. Journal of Consulting and Clinical Psychology, 85(9), 835–853. https://doi.org/10.1037/ccp0000231

17.

Dillon

P. J.

Kedia

S. K.

Isehunwa

O. O.

Sharma

(2020). Motivations for treatment engagement in a residential substance use disorder treatment program: A qualitative study. Substance Abuse: Research and Treatment, 14, 1178221820940682. https://doi.org/10.1177/1178221820940682

18.

Duffy

M. E.

Twenge

J. M.

Joiner

T. E.

(2019). Trends in mood and anxiety symptoms and suicide-related outcomes among U.S. undergraduates, 2007-2018: Evidence from two national surveys. The Journal of adolescent health: Official Publication of the Society for Adolescent Medicine, 65(5), 590–598. https://doi.org/10.1016/j.jadohealth.2019.04.033

19.

Eccles

J. S.

Wigfield

(2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153

20.

Eckshtain

Kuppens

Ugueto

M. Y.

Vaughn-Coaxum

Corteselli

Weisz

J. R.

(2020). Meta-analysis: 13-year Follow-up of psychotherapy effects on youth depression. Journal of the American Academy of Child & Adolescent Psychiatry, 59(1), 45–63. https://doi.org/10.1016/j.jaac.2019.04.002

21.

Elices

Pérez-Sola

Pérez-Aranda

Colom

Polo

Martín-López

L. M.

Gárriz

(2022). The effectiveness of mindfulness-based cognitive therapy in primary care and the role of depression severity and treatment attendance. Mindfulness, 13(2), 362–372. https://doi.org/10.1007/s12671-021-01794-3

22.

Essau

C. A.

Lewinsohn

P. M.

Olaya

Seeley

J. R.

(2014). Anxiety disorders in adolescents and psychosocial outcomes at age 30. Journal of Affective Disorders, 163, 125–132. https://doi.org/10.1016/j.jad.2013.12.003

23.

Fluckiger

Del Re

A. C.

Munder

Heer

Wampold

B. E.

(2014). Enduring effects of evidence-based psychotherapies in acute depression and anxiety disorders versus treatment as usual at follow-up--a longitudinal meta-analysis. Clinical Psychology Review, 34(5), 367–375. https://doi.org/10.1016/j.cpr.2014.05.001

24.

Goetter

E. M.

Frumkin

M. R.

Palitz

S. A.

Swee

M. B.

Baker

A. W.

Bui

Simon

N. M.

(2020). Barriers to mental health treatment among individuals with social anxiety disorder and generalized anxiety disorder. Psychological Services, 17(1), 5–12. https://doi.org/10.1037/ser0000254

25.

Golberstein

Gonzales

Meara

(2019). How do economic downturns affect the mental health of children? Evidence from the national health interview survey. Health Economics, 28(8), 955–970. https://doi.org/10.1002/hec.3885

26.

Graziano

Elbogen

E. B.

(2017). Improving mental health treatment utilization in military veterans: Examining the effects of perceived need for care and social support. Military Psychology: The Official Journal of the Division of Military Psychology, American Psychological Association, 29(5), 359-369. https://doi.org/10.1037/mil0000169

27.

Herrle

Angermeyer

M. C.

(1997). Psychotherapy instead of social support? The role of individual support network in the utilization of therapeutic offers. Zeitschrift Fur Klinische Psychologie-Forschung Und Praxis, 26(1), 58–67.

28.

Jaffe

A. E.

Walton

T. O.

Walker

D. D.

Kaysen

D. L.

(2023). Social support and treatment utilization for posttraumatic stress disorder: Examining reciprocal relations among active duty service members. Journal of Traumatic Stress, 36(3), 537–548. https://doi.org/10.1002/jts.22908

29.

Joyce

N. R.

Schuler

M. S.

Hadland

S. E.

Hatfield

L. A.

(2018). Variation in the 12-Month treatment trajectories of children and adolescents after a diagnosis of depression. JAMA Pediatrics, 172(1), 49–56. https://doi.org/10.1001/jamapediatrics.2017.3808

30.

Kazdin

A. E.

Holland

Crowley

Breton

(1997). Barriers to treatment participation scale: Evaluation and validation in the context of child outpatient treatment. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 38(8), 1051–1062. https://doi.org/10.1111/j.1469-7610.1997.tb01621.x

31.

Keeling

Barr

Atuel

Castro

C. A.

(2020). Symptom severity, self-efficacy and treatment-seeking for mental health among US Iraq/Afghanistan military veterans. Community Mental Health Journal, 56(7), 1239–1247. https://doi.org/10.1007/s10597-020-00578-8

32.

Kennedy

Anyon

Engle

Schofield Clark

(2021). Using intergroup contact theory to understand the practices of youth-serving professionals in the context of YPAR: Identifying racialized adultism. Child & Youth Services, 43(1), 76–103. https://doi.org/10.1080/0145935X.2021.2004113

33.

Keshen

Helson

Town

Warren

(2017). Self-efficacy as a predictor of treatment outcome in an outpatient eating disorder program. Eating Disorders, 25(5), 406–419. https://doi.org/10.1080/10640266.2017.1324073

34.

Kim

J. E.

Zane

(2015). Help-seeking intentions among Asian American and White American students in psychological distress: Application of the health belief model. Cultural Diversity and Ethnic Minority Psychology, 22(3), 311–321. https://doi.org/10.1037/cdp0000056

35.

Kim

J. E.

Zane

(2016). Help-seeking intentions among Asian American and White American students in psychological distress: Application of the health belief model. Cultural Diversity and Ethnic Minority Psychology, 22(3), 311–321. https://doi.org/10.1037/cdp0000056

36.

Klein

M. C.

Ciotoli

Chung

(2011). Primary care screening of depression and treatment engagement in a university health center: A retrospective analysis. Journal of American College Health, 59(4), 289–295. https://doi.org/10.1080/07448481.2010.503724

37.

LeCloux

Maramaldi

Thomas

Wharff

(2016). Family support and mental health service use among suicidal adolescents. Journal of Child and Family Studies, 25(8), 2597–2606. https://doi.org/10.1007/s10826-016-0417-6

38.

Lee

Zehgeer

Ginsburg

G. S.

McCracken

Keeton

Kendall

P. C.

Birmaher

Sakolsky

Walkup

Peris

Albano

A. M.

Compton

(2019). Child and adolescent adherence with cognitive behavioral therapy for anxiety: Predictors and associations with outcomes. Journal of Clinical Child and Adolescent Psychology, 48(sup1), S215–S226. https://doi.org/10.1080/15374416.2017.1310046

39.

Lehrbach

K. R.

Crane

M. E.

Olino

T. M.

Kendall

P. C.

(2023). Anxiety sensitivity and experiential avoidance: Relations with anxiety severity and treatment outcomes in anxious youth. Cognitive Therapy and Research, 47(5), 841–850. https://doi.org/10.1007/s10608-023-10408-2

40.

D’Arcy

Meng

(2019). Predictors of functional improvement in children and adolescents at a publicly funded specialist outpatient treatment clinic in a Canadian Prairie City. Psychiatry Research, 273, 613–623. https://doi.org/10.1016/j.psychres.2019.01.093

41.

Lilly

F. R. W.

Jun

Alvarez

Owens

Malloy

Bruce‐Bojo

Vidal

(2020). Pathways from health beliefs to treatment utilization for severe depression. Brain and Behavior, 10(12), e01873. https://doi.org/10.1002/brb3.1873

42.

Lizardi

Stanley

(2010). Treatment engagement: A neglected aspect in the psychiatric care of suicidal patients. Psychiatric Services, 61(12), 1183–1191.

43.

Lorian

C. N.

Grisham

J. R.

(2011). Clinical implications of risk aversion: An online study of risk-avoidance and treatment utilization in pathological anxiety. Journal of Anxiety Disorders, 25(6), 840–848. https://doi.org/10.1016/j.janxdis.2011.04.008

44.

McCarthy

K. S.

Keefe

J. R.

Barber

J. P.

(2016). Goldilocks on the couch: Moderate levels of psychodynamic and process-experiential technique predict outcome in psychodynamic therapy. Psychotherapy Research, 26(3), 307–317. https://doi.org/10.1080/10503307.2014.973921

45.

Mehra

Hawke

L. D.

Watson

Sheikhan

N. Y.

Leroux

Henderson

(2021). Youth perspectives on seeking psychotherapy: A concurrent mixed methods study. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 30(3), 165–176.

46.

Meredith

L. S.

Stein

B. D.

Paddock

S. M.

Jaycox

L. H.

Quinn

V. P.

Chandra

Burnam

(2009). Perceived barriers to treatment for adolescent depression. Medical Care, 47(6), 677–685. https://doi.org/10.1097/MLR.0b013e318190d46b

47.

Miles

M. M.

Szwedo

D. E.

Allen

J. P.

(2018). Learning to cope with anxiety: Long-term links from adolescence to adult career satisfaction. Journal of Adolescence, 64(1), 1–12. https://doi.org/10.1016/j.adolescence/2018.01.003

48.

Miller

L. M.

Southam-Gerow

M. A.

Allin

R. B.

(2008). Who Stays in Treatment? Child and Family Predictors of Youth Client Retention in a Public Mental Health Agency. Child & Youth Care Forum, 37(4), 153–170. https://doi.org/10.1007/s10566-008-9058-2

49.

Miranda

Soffer

Polanco-Roman

Wheeler

Moore

(2015). Mental health treatment barriers among Racial/Ethnic minority versus white young adults 6 months after intake at a college counseling center. Journal of American College Health, 63(5), 291–298. https://doi.org/10.1080/07448481.2015.1015024

50.

Monti

Cedereke

Öjehagen

(2003). Treatment attendance and suicidal behavior 1 month and 3 months after a suicide attempt: A comparison between two samples. Archives of Suicide Research, 7(2), 167–174. https://doi.org/10.1080/13811110301581

51.

Moore

K. L.

Lopez

Camacho

Munson

M. R.

(2020). A qualitative investigation of engagement in mental health services among Black and Hispanic LGB young adults. Psychiatric Services, 71(6), 555–561. https://doi.org/10.1176/appi.ps.201900399

52.

Newman

M. G.

Llera

S. J.

(2011). A novel theory of experiential avoidance in generalized anxiety disorder: A review and synthesis of research supporting a contrast avoidance model of worry. Clinical Psychology Review, 31(3), 371–382. https://doi.org/10.1016/j.cpr.2011.01.008

53.

Nobiling

B. D.

Maykrantz

S. A.

(2017). Exploring perceptions about and behaviors related to mental illness and mental health service utilization among college students using the health belief model (HBM). American Journal of Health Education, 48(5), 306–319. https://doi.org/10.1080/19325037.2017.1335628

54.

O'Connor

Bagnell

McGrath

Wozney

Radomski

Rosychuk

R. J.

Curtis

Jabbour

Fitzpatrick

Johnson

D. W.

Ohinmaa

Joyce

Newton

(2020). An internet-based cognitive behavioral program for adolescents with anxiety: Pilot randomized controlled trial. JMIR Mental Health, 7(7), Article e13356. https://doi.org/10.2196/13356

55.

O'Dor

S. L.

Washburn

Howard

K. R.

Reinecke

M. A.

(2021). Moderators and predictors of response after 36 weeks of treatment in the treatment for adolescents with depression study (TADS). Research on Child and Adolescent Psychopathology, 49(11), 1489–1501. https://doi.org/10.1007/s10802-021-00828-7

56.

Osuch

Vingilis

Summerhurst

Demy

Wammes

Arcaro

(2019). Process evaluation of a treatment program for mood and anxiety disorders among emerging adults: Preentry factors, engagement, and outcomes. Psychiatric Services, 70(3), 211–218. https://doi.org/10.1176/appi.ps.201800219

57.

Parker

Fletcher

(2007). Treating depression with the evidence-based psychotherapies: A critique of the evidence. Acta Psychiatrica Scandinavica, 115(5), 352–359. https://doi.org/10.1111/j.1600-0447.2007.01007.x

58.

Rickwood

Deane

F. P.

Wilson

C. J.

Ciarrochi

(2005). Young people's help-seeking for mental health problems. Advances in Mental Health, 4(3), 1446–7984.

59.

Schuler

M. S.

Gilman

S. E.

Burns

R. M.

Roth

Breslau

(2021). Associations between depression subtype and functional impairment and treatment utilization in a national sample of adolescents. Journal of Affective Disorders, 287, 26–33. https://doi.org/10.1016/j.jad.2021.03.018

60.

Seligman

L. D.

Hovey

J. D.

Ibarra

Hurtado

Marin

C. E.

Silverman

W. K.

(2020). Latino and non-latino parental treatment preferences for child and adolescent anxiety disorders. Child Psychiatry and Human Development, 51(4), 617–624. https://doi.org/10.1007/s10578-019-00945-x

61.

Sherman

L. J.

Ali

M. M.

(2017). Mothers’ mental health treatment associated with greater adolescent mental health service use for depression. Journal of Child and Family Studies, 26(10), 2762–2771. https://doi.org/10.1007/s10826-017-0781-x

62.

Shorey

E. D.

Wong

C. H. J.

(2022). Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis. British Journal of Clinical Psychology, 61(2), 287–305. https://doi.org/10.1111/bjc.12333

63.

Smith

T. W.

(2009). If we build it, will they come? The health belief model and mental health care utilization. Clinical Psychology, 16(4), 445–448.

64.

Stafford

A. M.

Draucker

C. B.

(2020). Barriers to and facilitators of mental health treatment engagement among Latina adolescents. Community Mental Health Journal, 56(4), 662–669. https://doi.org/10.1007/s10597-019-00527-0

65.

Starcevic

(2006). Review: Worldwide lifetime prevalence of anxiety disorders is 16.6%, with considerable heterogeneity between studies. Evidence-Based Mental Health, 9(4), 115. https://doi.org/10.1136/ebmh.9.4.115

66.

Steuwe

Berg

Driessen

Beblo

(2023). Therapeutic alliance and dropout in patients with borderline pathology receiving residential dialectical behavior therapy. BMC Psychiatry, 23(1), 1–605. https://doi.org/10.1186/s12888-023-05061-8

67.

Stochl

Soneson

Stuart

Fritz

Walsh

A. E. L.

Croudace

Hodgekins

Patel

Russo

D. A.

Knight

Jones

P. B.

Perez

(2022). Determinants of patient-reported outcome trajectories and symptomatic recovery in Improving Access to Psychological Therapies (IAPT) services. Psychological Medicine, 52(14), 3231–3240. DOI:10.1017/S0033291720005395

68.

Thompson

Issakidis

Hunt

(2008). Delay to seek treatment for anxiety and mood disorders in an Australian clinical sample. Behaviour Change, 25(2), 71–84. https://doi.org/10.1375/bech.25.2.71

69.

Tricco

A. C.

Lillie

Zarin

O'Brien

K. K.

Colquhoun

Levac

Moher

Peters

M. D. J.

Horsley

Weeks

Hempel

Akl

E. A.

Chang

McGowan

Stewart

Hartling

Aldcroft

Wilson

M. G.

Garritty

Straus

S. E.

(2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850

70.

Upadhyay

Aparasu

Rowan

P. J.

Fleming

M. L.

Balkrishnan

Chen

(2019). The association between geographic access to providers and the treatment quality of pediatric depression. Journal of Affective Disorders, 253, 162–170. https://doi.org/10.1016/j.jad.2019.04.091

71.

Visla

Allemand

Fluckiger

(2023). Within- and between-patients associations between self-efficacy, outcome expectation, and symptom change in cognitive behavioral therapy for generalized anxiety disorder. Journal of Clinical Psychology, 79(1), 86–104. https://doi.org/10.1002/jclp.23407

72.

Wilansky-Traynor

Manassis

Monga

Shaw

Merka

Levac

Kleiman

(2010). Cognitive behavioural therapy for depressed youth: Predictors of attendance in a pilot study. Journal of the Canadian Academy of Child and Adolescent Psychiatry = Journal De L'Academie Canadienne De Psychiatrie De L'Enfant Et De L'Adolescent, 19(2), 81–87.

73.

Wisdom

J. P.

Clarke

G. N.

Green

C. A.

(2006). What teens want: Barriers to seeking care for depression. Administration and Policy in Mental Health, 33(2), 133–145. https://doi.org/10.1007/s10488-006-0036-4

74.

Hoven

C. W.

Cohen

Liu

Moore

R. E.

Tiet

Okezie

Wicks

Bird

H. R.

(2001). Factors associated with use of mental health services for depression by children and adolescents. Psychiatric Services, 52(2), 189–195. https://doi.org/10.1176/appi.ps.52.2.189

75.

Zarger

M. M.

Rich

B. A.

(2016). Predictors of treatment utilization among adolescents with social anxiety disorder. Children and Youth Services Review, 71, 191–198. https://doi.org/10.1016/j.childyouth.2016.11.011

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.37 MB