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Abstract

Despite high prevalence rates of pediatric mental health challenges, estimates suggest over 50% of youth with mental health conditions have never accessed services. eMental health resources have increased over the past two decades to mitigate barriers to access. Positive attitudes towards eMental health resources have been reported in adults; however, little research has focused on parent perceptions of pediatric eMental health resources. The current study explored parent perceptions of matched eMental health resources for their child using machine learning algorithms. Parents were recruited from a longitudinal mental health study; 49 parents participated in a semi-structured individual virtual interview. Interpretative description analysis generated two primary themes: (1) feelings on AI use to assign matched mental health resources and (2) parent perceptions of eMental health resources for their child. Findings support a general acceptance by parents of the integration of AI in assigning and providing them with mental health resources for their child contingent on clinician oversight. This study fills a distinct gap in the literature on parent perceptions of pediatric eMental health resources and the role of AI, which are integral to consider in the development and dissemination of resources.

Plain Language Summary

The number of children experiencing mental health challenges continues to rise, yet research has found that over 50% have never access mental health resources. Virtual mental (eMental) health resources have been found to decrease barriers to accessing and using resources; however, this research has primarily focused on adults seeking out services for themselves. It remains unclear how parents feel about eMental health resources for their children as well as the use of AI to develop and assign these resources. The current study explored parent perceptions of matched eMental health resources for their child using machine learning algorithms, which is a subtype of artificial intelligence (AI). Forty-nine parents participated in virtual interviews, which were analyzed using interpretative description analysis. Analysis generated two primary themes: (1) feelings on AI use to assign matched mental health resources and (2) parent perceptions of eMental health resources for their child. The findings suggested a general acceptance by parents of the use of AI to develop mental health profiles of their child and assign them appropriate virtual resources. These findings fill an existing gap in the literature on parent attitudes towards AI in mental health targeting their child and eMental health resources that can direct future development and distribution of resources.

Keywords

eMental health resources mental health virtual resources machine learning algorithms qualitative research parent perceptions

Accessibility and uptake have been identified as the two primary barriers to the use of mental health resources by youth. Research indicates that while mental health programs may be available, they are not currently being widely accessed, and most youth are not receiving treatment for their mental health difficulties (Cross et al., 2022; Etzelmueller et al., 2020; Hintzpeter et al., 2015; Reardon et al., 2017; Stiles-Shields et al., 2023). Specifically, some research across the United States of America and Europe suggests that more than 50% of youth with a mental health disorder report that they had not previously accessed mental health services (LeCloux et al., 2017). In Canada, it has been suggested that while 15-25% of youth experience mental health concerns, only a 20% of those (3-5% of the total population) are able to access, or are accessing, clinical resources (Toulany et al., 2022). This suggests a two-fold barrier to the uptake of youth mental health resources: availability and accessibility. That is, existing research may be indicative of existing resource not being available (e.g., due insufficient number of programs) or youth or families not perceiving them to be accessible (e.g., geographical location), thereby decreasing their uptake (Radez et al., 2021). However, the specific factors that may impact the uptake of resources by youth experiencing mental health concerns is unclear. Additionally, perceptions of the appropriateness or acceptability of existing resources is largely unknown. Additional research is necessary to better understand the low uptake of mental health resources despite the high prevalence rates of mental health concerns in youth (Radez et al., 2021).

The World Health Organization reported in 2006 that the integration of technology into healthcare and services encourages individual involvement and autonomy over health-related decisions (Muhammad & Achadi, 2024). Telehealth was introduced into healthcare discussions in 1964, (Ikumapayi et al., 2022); however, researchers have suggested that telemedicine (used interchangeably with telehealth) was first found in medical literature in 1974 (Zundel, 1996). While it initially referred to communicating healthcare information between health care practitioners, this definition has evolved with technology since its inception (Muhammad & Achadi, 2024; Zundel, 1996). Over the past two decades, research investigating the role of electronic methods of mental health delivery (eMental health) have increased globally (Weitzel et al., 2023). The term eMental health was coined to define the provision of mental health services using digital technologies (March et al., 2018; Musiat et al., 2014). Numerous advantages of eMental health services have been identified (Abuyadek et al., 2024); specifically, research has noted they increase the flexibility and accessibility of mental health resources, reduce stigma associated with mental health (Musiat et al., 2014), increase anonymity (Weitzel et al., 2023), decrease wait-times, and increase the affordability of mental health services (Illman, 2004; Spurgeon & Wright, 2010). EMental health has therefore been suggested to mitigate some barriers to resource uptake (Ikumapayi et al., 2022; Weitzel et al., 2023; Wetterlin et al., 2014). However, little research has investigated beliefs, feelings about and/or perceptions of eMental health services; those available have primarily included adult samples and results have been mixed (Dimitropoulos et al., 2024). In adult samples, research suggests face-to-face services are favored and perceived as more trustworthy when compared to eMental health options (March et al., 2018). However, research has demonstrated a willingness by youth to engage in eMental health treatment (Hawke et al., 2021).

Some research has investigated parent utilization of eMental health services that target their child’s challenges related to mental health. Specifically, Sweeney and colleagues (2015; 2017) found that in a community sample in Australia, 66% of parents reported current or past mental health problems experienced by their child, of which anxiety (39.1%) and behavioural problems (30.6%) were the most common. Notably, mental health challenges were broadly defined, and included struggles such as bullying, as well as neurodiverse conditions, such as autism (Sweeney et al., 2015). When asked about eMental health use, only 50% reported seeking out support for their child’s challenges, of which only 6% indicated they had accessed eMental health resources (Sweeney et al., 2015). When asked about eMental resources, parents identified virtual resources as the service they were least likely to use and described a lack of knowledge regarding virtual services (Sweeney et al., 2015, 2017). Notably, these studies were completed prior to COVID-19, and since then many virtual services and modalities for services have become more mainstream; these perceptions therefore may not be replicated in current populations. Importantly, the current research on feelings/beliefs towards eMental health services have primarily included adult samples who participated for their own mental health. Few studies have focused on parent perceptions, and therefore little is understood about how parents feel about eMental health services for their children (Eapen et al., 2021; Pine et al., 2024; Sweeney et al., 2017). This gap in the literature requires attention given that parents often support and facilitate their child’s engagement in any services, thereby dictating treatment access (Horwitz et al., 1998; Sweeney et al., 2017).

More recently, various methods of machine learning algorithms and artificial technology (AI) has been integrated into some aspects of mental health care (Andrew et al., 2023; Marshall et al., 2025). Machine learning algorithms are, in lay terms, a simpler version of AI whereby mathematical algorithms dictate pre-determined or scripted responses or steps (Srividya et al., 2018). While machine learning represents a specific subset of artificial intelligence, it is often categorized under the broader umbrella of AI and sometimes grouped alongside more advanced applications by lay persons (Kühl et al., 2022). AI technologies have been predominantly provided via mobile mental health applications and AI-generated responses in conversation-style interactions (Varghese et al., 2024).

AI has numerous capabilities, including the review and interpretation of data, and the identification of trends, enabling it to generate data-informed recommendations or associations (Marshall et al., 2025). Furthermore, AI has been integrated into mental health care to provide mental health support and offer modified virtual services for adults and youth (Andrew et al., 2023; Marshall et al., 2025). Mental health services, and assignment to mental health treatment, has further incorporated AI to analyze numerous factors contributing to mental health challenges and/or conditions, including psychological and social considerations (Marshall et al., 2025). Notably, an identified barrier to further AI integration within mental health disciplines includes possible errors within AI responses and/or tools that may introduce risk within the provision of mental health care, especially to youth (Marshall et al., 2025). There remains some distrust and lack of knowledge surrounding AI in the context of mental health services (Aktan et al., 2022), and preference towards contact with human clinicians as opposed to AI platforms has been reported among adults (Aktan et al., 2022; Varghese et al., 2024). Specifically, concerns were raised related to the security of personal information and data with the integration of AI components (Aktan et al., 2022) as well the perceived risk for misdiagnoses and negative impact of decreased human interactions (Varghese et al., 2024). Notably, the available studies to date have predominantly investigated the perceptions of adults regarding mental health services for themselves; reported perceptions of AI, including feelings of distrust and concern, have therefore primarily been expressed by adults when considering services targeting adult populations. There are few studies investigating parent perceptions of the integration of AI functions in assigning appropriate mental health resources for challenges experienced by their child; attitudes are integral to understand as they are instrumental in facilitating or barring use and uptake (Higgins et al., 2023; Varghese et al., 2024; Yunike et al., 2023).

The Current Study

The current study addressed the gap in the literature by taking a qualitative approach to understanding parent perceptions of eMental health resources for their children that are matched to their child using a machine learning algorithm (AI). The primary aim of the current study was to obtain a better understanding of how parents feel about the use of eMental health resources that target mental health concerns in their child. Importantly, the current study was developed from a longitudinal project that used machine learning algorithms to assign appropriate mental health resources targeting the mental health challenges reported in the child. The focus and analysis of the current study has therefore been developed based on the design of the original study; specifically, the focus on AI-assisted resource matching reflects the matching process between child mental health challenges and eMental health resources in the longitudinal study. Notably, the focus of the current study is therefore narrower than the broader landscape of how AI continues to be applied within mental health care for youth and eMental health resources for children and adolescents. Given the lack of literature specifically focused on parent perceptions of eMental health resources utilized AI functions to match and assign eMental health resources for children, no hypotheses were identified. Qualitative inquiries are critical to attaining detailed information from individuals who have first-hand experience of a phenomenon (Thorne et al., 1997) and therefore are necessary to inform future research and procedures surrounding the provision of eMental health resources for families.

Methods

Study Background

The participants included in this study were recruited from a longitudinal study conducted in 2021 focused on mental health in neurodiverse and neurotypical children [The Matching Study]. Clinically diagnosed conditions in the child were parent-reported; neurodevelopmental conditions (e.g., autism) were reported by some parents (i.e., neurodiverse), while no conditions were reported by other parents (i.e., neurotypical). This study provided parents with eMental health resources depending on the specific mental health challenges they identified in their child. Specifically, the Matching Study was conducted across three timepoints and employed machine learning algorithms to assign mental health profiles to children based on completed parent-report questionnaires. The profiles were subsequently matched to appropriate mental health resources and provided to the families (Korczak et al., 2022). Parents whose children did not meet clinical threshold were offered psychoeducational resources via trusted hospital-based websites designed to offer support for children during COVID. Those whose children were reported to have clinically significant symptoms were offered clinical services provided online that targeted internalizing or externalizing behavioural problems (e.g., modified cognitive behavioural therapy). Specifically, children were who assigned an internalizing profile (defined through depression and anxiety mental health challenges) were matched with a modified online cognitive behavioral therapy. Children who were assigned either the externalizing (defined through inattention, hyperactivity, and depression challenges) or struggler (combination of internalizing and externalizing challenges) profiles were assigned to either the I-InTERACT North eMental health program for children between 3-9 years of age, or the Coping Power Program for children 9-12 years old. The enrollment timepoint (T0) reflected participant interest and subsequent consent to participate in the Matching study. Parent enrollment and the completion of consent materials was considered to reflect initial parent interest in participating in the Matching study. Participants subsequently completed a questionnaire package at timepoint one (T1) that was utilized for machine learning algorithms to generate a mental health profile for the child. Additional questionnaires were provided to participants at timepoint two (T2), as well as the eMental health profile that was assigned to their child and the matched resource. Finally, at timepoint 3 (T3) participants were asked about how they accessed and used the provided eMental health resource. All participants (n = 292) from the Matching Study were invited to participate in the current study.

Participants

Participants were recruited from The Matching study - a longitudinal investigation conducted in 2021 focused on mental health in children during COVID-19. Notably, the current study is specifically investigating parent perceptions of eMental health services; the specific matches are therefore not the focus of this paper. One-hundred-and-five parent participants (35.34%) consented to participate in the interview regarding parent perceptions of eMental health services, 42 of which completed T0 (40%), 22 completed T1 (20.95%), 7 completed T2 (6.67%), and 34 completed T3 (32.38%) of the previous Matching study. From the 105 consenting parents, 49 completed the interview (46.67%) for the current study. Of those who completed the interview, 25 (51.02%) enrolled but did not complete any questionnaire packages (T0), 10 had completed T1 (20.41%), three had completed T2 (6.12%), and 11 had completed T3 (22.49%) of the previous Matching study. Demographic details on the participants who completed the interview are reported in Table 1.

Table 1.

Parent-Reported Demographic Details of Those Who Completed the Interviews are Detailed Below

Descriptive variables - parent	Interviewed participants
Responding parent education
Some grade school	0
Some high-school	0
High school diploma/GED	0
Some college/university	7
College/university degree	14
Some school beyond college/university	8
Graduate/professional degree	16
Non-responding parent education
Some grade school	0
Some high-school	1
High school diploma/GED	2
Some college/university	7
College/university degree	19
Some school beyond college/university	2
Graduate/professional degree	15
Geographical origin
European	31
Non-European	10
Mixed	8
Annual household income
Greater than 80k	31
Less than 80k	10
Parent relationship to child
Mother	41
Father	8
Grandparent	0
Descriptive variables - child	Interviewed participants
Child sex
Female	26
Male	22
Not provided	1
Mean child age at enrollment (T0) in matching study	8.61 (years)
Child diagnosis
Autism	4
ADHD	11
Anxiety	10
OCD	1
Depression	0
Intellectual disorder	2
Learning disorder	7

Procedure

An email invitation to participate in the interview for the current study was sent to all participants who participated in the Matching Study and had consented to future contact regarding research opportunities (n = 292). The email was sent via the web-based platform Research Electronic Data Capture - REDCap (Harris et al., 2009, 2019), which was used for the Matching Study and therefore familiar to participants. Participants who did not consent to participate did not provide reasons behind their decision. Following consent, the first author reached out to participants via email to schedule a zoom interview at a mutually convenient time. Multiple communications were made to establish contact with participants following consent; participants who remained unreachable and did not complete the interview did not provide reasons behind their decisions. All interviews were audio recorded through the Zoom platform and subsequently transcribed. Ethics for this study was approved by the HSREB primary research committee at both Queen’s University (#6037663) and Sick Kids Research Hospital (#1000070222). Informed written consent was provided by all parent participants. Participants consented to their data being used and published, including deidentified verbatim quotes.

Measures

Demographic Questionnaire

All demographic information was collected at the time of enrollment in the Matching Study. The demographic questionnaire consisted of questions regarding the child’s age, sex at birth, ethnicity, household annual income, parent education, and pre-existing mental health conditions. Participants completed the demographic questionnaire on the REDCap platform, which allowed them to be completed at the participants’ convenience.

Interview

An author-developed interview was created for the purpose of this study that included questions focused on participant experiences and reasons behind participant attrition. Questions for this interview were developed using the Ecological Theory of Attrition (ETA (Marcellus, 2004); as the theoretical framework. The ETA organizes factors related to participant experiences across different levels of proximity: the participant, the researcher, the study details, and the environment, all factors relevant to the research question. For example, participants were asked questions regarding the matching process and their perceptions of the matched eMental health resource (e.g., What, if any, expectations did you have about the matching process and the resource your child was matched with? How did you feel specifically about the computer-generated match for your child’s mental health resource?).

Data Analysis

Guidelines remain limited regarding necessary sample sizes for qualitative research; it is instead often determined based on the study approach, methods, and feasibility (Guest et al., 2017; Wutich et al., 2024). The rationale behind the target sample size of the current study was based on obtaining perceptions representative of participants with different completion rates of the original longitudinal study. Further, the sample size was constricted to remain feasible to complete as a doctoral dissertation and ensure completion within a 4-year period. The target sample size for this study was therefore 50 participants, including a minimum target of 10 participants from each timepoint. This was a flexible target; the sample size was adaptable based on the methods and ongoing analysis. Specifically, the continuous and concurrent nature of data collection and analysis using interpretative description analysis enabled the continuous review of developing findings to identify when data saturation was reached. Therefore, the target sample size remained flexible so as to allow for more or less interviews to be collected based on the development of the findings (i.e., if data saturation was not reached by the target sample size, more interviews could be conducted).

An inductive approach was maintained throughout data analysis (Thorne et al., 1997). Interpretative description analysis was employed; this method of qualitative analysis allows flexibility in the data collection and analysis process in which continued review of the data is completed and associated revisions are able to be integrated. Further, this method of analysis allows for the clinical experience of the researchers to be considered throughout the study and analysis. Aligning with this method of analysis, the first five interviews were reviewed by the first author and discussed with researchers with clinical experience. During this review, discussed factors were identified within parent discussions that were not included within the initial interview guide as questions or prompts. Based on the participant discussions and collaborative review of these transcripts, three additional questions were incorporated.

Interviews were transcribed by trained undergraduate students and reviewed throughout the research process to develop an initial codebook which was reviewed and revised by the researcher team. Coding was completed using NVivo-Version 14 (Lumivero, 2023). The sample did not permit for comparison across completed timepoints; there was an unequal distribution of participants from each completion timepoint that did not allow for comparison. There was further a range of parent-reported child conditions; however, the distribution of parent-reported conditions and scope of the study did not support comparison of perceptions. A review of the initial codes was nevertheless completed; there were no substantial differences in the generated codes across conditions or completion rates. Persistent observation through concurrent data collection and analysis was completed and data saturation was achieved. Collaborative team meetings to review and discuss the data and the generated codes were completed, through which a final codebook was generated. The generated codes were organized into subthemes and themes to reflect the participants’ data.

Trustworthiness

Ensuring scientific rigour is necessary in qualitative research and is referred to as trustworthiness (Lincoln, 1985; Lincoln & Guba, 1982). We used three methods of trustworthiness: persistent observation of the data, investigator triangulation and reflexivity. The data were continually observed throughout data collection and reviewed multiple times throughout data analysis. Triangulation refers to the use of multiple information sources or procedures to ensure agreement in procedural decisions and the generated results (Cope, 2013; Fossey et al., 2002; Stahl & King, 2020); multiple researchers with different areas of expertise across institutions consulted throughout the data analysis process. Specifically, during the analytical process, biweekly meetings were held between the primary coders (LD & EK) and monthly discussions were held with the entire research team. These meetings enabled full transparency through the analytical process in addition to collaborative group discussions regarding the codes identified, in addition to critical discussion of the organization of codes into categories. Differing perspectives regarding codes were discussed with the entire research team and consensus reached on developed codes and subsequent themes. Reflexivity involves researcher reflection on any preconceptions throughout the study process that may have been influential on the results (Berger, 2015; Korstjens & Moser, 2018); this was completed individually by the first author as well as through group discussions with all authors. Further, the primary coders (LD & EK) completed positionality statements that were reviewed throughout the analytical process and reflected upon during team discussions.

Findings

The participants who completed the interview (n = 49) predominantly identified as European (63.27%) with at least one parent (77.55%) reporting a college degree or higher. Over half of our sample further indicated a household income over eighty thousand dollars Canadian (63.27%). Parents further reported any previous diagnoses applied to their child; all demographics are reported in Table 1.

The findings discussed in this manuscript are part of a larger dissertation study; discussions related to AI and eMental health resources discussions are included. The data generated two themes related to eMental health resources: (1) feelings regarding AI use to develop mental health profiles and assign relevant resources and (2) general perceptions regarding eMental health resources that targeted their child’s mental health profile. De-identified quotes are included throughout the results to illustrate the themes identified. Consent was obtained by participants to present de-identified direct quotes.

Theme 1: Feelings regarding AI Integration to Support the Development of Mental Health Profiles and Resource Assignment

Participants predominantly expressed positive feelings when asked about the integration of AI in eMental health service provision, however some concerns were raised. Discussions regarding the use of AI were organized into two subthemes: (1) positive feelings towards AI and perceptions of its utility in the future, and (2) concerns surrounding the use of AI when assigning and providing eMental health resources to youth.

Subtheme 1: Positive Feelings Surrounding AI

Participants predominantly expressed positive feelings (e.g., comfort) when asked how they felt about AI being used to develop their child’s mental health profile and subsequently match that profile with an eMental health resource. The openness towards AI was expressed by one participant who stated, “I think I’m all in it for it […] because I heard that […] AI is now the future so I think this is some kind of AI so I am all in […] for it” (Participant #17). In fact, some participants expressed appreciation and excitement over the use of AI, as one described the use of AI as, “I think that’s brilliant I mean […] yeah […] if you’re looking for data […] from a specific type of profile then that seems like a good way to do it” (Participant #22). The benefits of AI were therefore recognized by most participants and acknowledgment, if not excitement, was expressed about the integration of AI into the provision of eMental health resources. AI was further positively viewed and perceived as acceptable if it were to be developed by mental health professionals. That is, participants articulated that they would be more comfortable with the integration of AI that supported profile development, assignment, and matched resource, if it was developed and overseen by psychological professionals. The excitement for AI was expressed by one participant who said, “Oh my gosh I mean that would be priceless. Yes, I would fully support that” (Participant #9).

Anonymity was further identified as a benefit of the integration of AI. Specifically, they felt that AI and virtual study components increased the level of perceived anonymity, and anonymity was desired. Therefore, AI was described to provide the perception of privacy and anonymity when answering questions related to mental health, which participants felt enabled them to provide honest answers reflective of their actual feelings and experiences. The benefits of AI were described by one participant who stated,

“you know, […] the thing is when you get something […] that feels kind of computer generated asking you personal questions […] it sounds more impartial like you’re not, I’m not talking directly to a person, right, and […] I’m able to answer questionnaires without thinking about, […] you know human stuff, right. […] I know some people can be really, really anxious or whatever and you know feel like you’re being judged and that sort of thing and maybe that plays into it, I mean I don’t personally feel that but it’s just easier if, you know, if we keep, keep things light […] yeah, just[…] makes it easy for me to […] you know, to fully participate I guess” (Participant #1).

The perceived benefit of anonymity was especially emphasized when parents considered the participation in mental health research for their child, and associated use of eMental health resources. Specifically, parents felt that the anonymity created through the integration of AI is especially beneficial for younger samples, such as their children/adolescents, who associated AI with increased anonymity which would result in more honest responses.

Subtheme 2: Concerns Surrounding the Use of AI When Assigning and Providing eMental Health Resources to Youth

Notably, participants identified some aspects of AI that they felt were limitations and elicited some concern. Specifically, participants felt AI was acceptable to be utilized within research capacities. AI was not however perceived to be a reliable replacement to a trained professional, as was described by a participant who stated, “[AI is] not reliable but an interesting tool as an assessment. I don’t think it should […] replace a medical assessment and one-on-one, but I think, I think as an initial sort of assessment tool it’s fine” (Participant #32). Concern was expressed regarding the exclusive use of AI, and some participants felt that it should be overseen by educated professionals. Participants advocated for the oversight of AI due to concerns regarding the stringency associated with the code making up the AI program. Specifically, they were concerned that this code would limit the efficacy creating mental health profiles and accurately matching them to relevant resources. These limitations were voiced by one participant who said,

“I mean in general it really depends on the limitations of the system […] I don’t have a problem if […] there’s a sufficient effort in programming it to ensure that it makes some sense, but, […] I don’t believe that I was given any specifications […] In general, I don’t mind, however, I have reservations […] even if you play with ChatGPT today, there are limitations, it can sometimes tell you something that’s completely wrong. It can be biased, it can be offensive, and […] that’s the cutting edge of AI today so […] these limitations are there for everything […] I would use it with a grain of salt. I wouldn’t […] totally 100% subscribe to it and […] I don’t think, […] especially considering that there was never anybody talking to us and the limits of the information that was collected originally, it wasn’t very thorough […] it was very limited in its scope” (Participant #20).

AI capabilities were especially concerning for parents with children experiencing diverse mental health challenges and associated needs. That is, some participants articulated concerns that these diverse challenges would not fit into pre-defined boxes, categories, or descriptions created by AI and therefore accurate assessment and categorization by AI would not be feasible. This was feared to lead to inaccurate assessments and inappropriate provision of resources. In sum, while most participants voiced a degree of comfort with AI integration into mental health research and resource provision, it was not fully accepted as an exclusive tool. Additionally, although AI has become much more mainstream, not all participants understand AI and confusion surrounding its role in research or mental health resource provision was a topic of confusion.

Theme 2: Parent Perceptions and Uptake of eMental Health Resources

Though eMental health resources were generally well accepted by participants, there was not a clear understanding of how to use them. That is, while many of the participants were provided a mental health profile for their child and a matched virtual mental health resource, some were not aware of the match or the resource, despite records indicating they received them. Specifically, participants reported they “don’t actually remember what they [the match] were” (Participant #8). The participants did remember receiving their child’s mental health profile and their associated matched resource. They were further unsure exactly what being matched meant and what the resources they were provided included. The lack of understanding by participants was highlighted by one participant who said,

“I don’t recall receiving it. I get many many many emails […] I don’t know if I can speak to that I’ll have to look that up I guess and see if I, unless I did, see if I received one … what does it look like? Is it an email that originates […] from school itself?” (Participant #39).

Evidently, there was notable confusion regarding the process of the match and resource provision. In fact, some participants were not even aware that the research study they were participating in provided eMental health resources and voiced a desire for them when informed.

Conversely, those who did recall receiving their matched resource reported mixed feelings about both the profile their child received and the associated matched resources. The match and resource were described by most participants as acceptable, accurately reflecting their child’s mental health. The perception towards the matched profile was described by one participant who stated “it’s been a while, but I thought it was an accurate match […] the match was actually pretty good because it was some of the same things I was thinking about my child” (Participant #34), while the matched resource was described “legit. I thought that, you know, the results came in a timely manner. I don’t know, I thought it was useful” (Participant #46) by another. Another participant described the resource they were give as “very thorough. I thought […] what was offered or […] recommended or suggested or provided, […] all seemed […] legitimate, thoughtful resources, so I thought it was good” (Participant #46). Notably, some participants did not use the resources they were provided due to feelings of them being unnecessary; “[it is] beneficial to have the information available if needed but [child] wasn’t one of these kids during COVID that sort of fell down the rabbit hole […] and deteriorated, […] I think, you know, she managed it well” (Participant #18). Further, this participant indicated they “might not need it today but I might need it in six months and then I can go back in my emails and find it” (Participant #18), describing the utility of the resources following the completion of their involvement in the study.

Participants expressed gratitude towards receiving mental health resources, as they identified that “anything is helpful […] for me when I’m looking for support for the kids […] I’ve been having a hard time finding […] in-person support for my kids since coming out of the pandemic” (Participant #48). Support for mental health concerns in their children was identified as challenging to access, and therefore all resources were welcomed and labeled as needed. Specifically, one participant described the resources they obtained as “a good starting point, that later they can reach out also to the doctor and discuss all the opportunities but it’s [resources provided] a good starting point” (Participant #38). Though most participants perceived the resources as useful tools, they did not report actively using them.

Negative case analysis identified a number of participants who did not perceive the matching process, or provided resources, positively. Specifically, one participant appeared confused by their child’s match, stating that “the matching part was just kind of random” (Participant #20). Further, a single participant felt that their match was not only inaccurate, but also stigmatizing towards their child. This participant voiced concern over the perception that the matched profile and associated eMental health resource pathologized their child, stating that,

“I don’t remember exactly what expectations I had, but I […] remember having a, a disconnect between the […] the expectations. […] When you tell me something is a resource that could help, […] my child, and obviously it is a sensitive thing about […] my kid’s mental health […] this is sensitive information I don’t want kind of obviously being shared outside of that. […] My contribution to science is kind of the only reason why I give that out and […] and so I think— having a referral to a, no it wasn’t, it wasn’t a referral to a psychiatrist, […] there was something about it that was just […] really not a resource […] it was pathologizing, it was […] literally something like […] if you want anything then you have a problem basically. That was the issue, […] there was something about, […] when I think something is a resource for my mental health it’s like you know you, you go and have a free meditation group, whatever, and then you get it and it doesn’t mean that all of a sudden you are on record as having a pathology and that was the vibe I got from whatever the computer generation thing brought to me […] and that’s what I didn’t want” (Participant #20).

Therefore, while most participants welcomed their child’s assigned match and associated mental health resources, this feeling was not shared by all participants.

Discussion

The findings indicate a general curiosity and an accepting attitude by parents towards the integration of AI and movement towards virtual and electronic methods of providing mental health services for children and adolescents. That is, parents were largely open to the integration of AI and provision of mental health services virtually despite some lack of understanding or residual concerns that supports a need for increased education. Specifically, parents articulated some concerns and lack of understanding regarding the role of AI and how it is integrated into the process of providing appropriate resources; however, this concern did not yield a negative attitude towards the integration of AI and path towards eMental health resources and was instead identified as an area requiring further attention. Parents welcomed accessible and appropriate resources and mental health support for their child, articulating the severe need for any services available that help them to help their child. Notably, the confusion regarding what resources were provided and how they could be accessed was identified as an area that required further improvement. While low uptake of the resources was reported, parents emphasized the gratitude they had towards receiving and having the resources provided; even if they weren’t using them now, they wanted to have them for when, not if, they would need them.

This study fills a notable gap in the literature; few studies to date have investigated parent perceptions of eMental health resources, or the integration of AI technology in assigning matched eMental health resources for youth (Sweeney et al., 2017). While previous research has included primarily adult samples that are seeking out resources for themselves (Dimitropoulos et al., 2024), the current study extends inquires to the perceptions of parents receiving eMental health resources via research that targets mental health concerns in their child. The perceptions demonstrated in these findings indicates that while AI is a recognized term, parents are simply not familiar enough with AI programming, and did not understand how it was specifically integrated into the resource allocation in the current study, to be fully comfortable. Notably, the anonymity associated by participants with AI and virtual resources was preferred by participants. Specifically, parents felt that the anonymity associated with providing information pertaining to mental health concerns to a system in which AI technology reviews the information, develops the mental health profile, and assigns a relevant eMental health resource, may generate more honest responses to mental health questions from themselves and their children. Honest responses were further recognized to support the allocation to appropriate resources; therefore, methods to increase anonymity and honesty in responses was welcomed. This extends previous research by Weitzel and colleagues (2023) who identified increased anonymity as an advantage of eMental health resources. Importantly, response accuracy on mental health-related questions may support the provision of appropriate resources that are better aligned with the child’s experienced challenges. Ensuring honesty in responses is critical, therefore consideration of increased perception of anonymity and associated honesty in responses related to mental health requires further exploration. The perceptions of eMental health, especially as the pertain to increased anonymity and honest responses, may further be important for clinicians to consider for youth assessments.

Importantly, participant acceptance of the use of AI technology in relation to eMental health resources was conditional on the oversight by a mental health professional. Specifically, while technology is increasingly accepted, there is still an expectation that trained professionals both develop any AI program and the associated eMental health resource, as well as continually oversee them. Therefore, while findings do not align with previous findings suggesting that face-to-face mental health services are more favourably perceived compared to eMental health services (March et al., 2018), they do indicate that it is preferred if AI and eMental Health options are reviewed and developed by human professionals. These findings may therefore support the targeted integration of information related to the use of AI within study materials and study information presented to families. Additionally, these findings suggest the integration of AI functions may be perceived as acceptable within the mental health discipline; this may be important for clinicians to consider when developing and using diagnostic measures.

Findings further support studies that have reported a perceived acceptance of eMental health resources (Baldofski et al., 2019; Landman et al., 2024; Limpanopparat et al., 2024; Sweeney et al., 2017; Wetterlin et al., 2014) and extend these findings to include eMental health resources provided to parents that target their child’s mental health challenges and needs. Specifically, parent reports align with the lack of resource uptake reported across the literature (Radez et al., 2021; Reardon et al., 2017) and provide a broader understanding regarding perceived utility of the resources. Despite a lack of use, there was distinct appreciation in having the resources available to reference when needed in the future. Findings therefore support previous reports identifying the lack of uptake and extend them to identify maintained appreciation and desire surrounding the resources. These results may therefore further explain the lack of uptake of mental health resources that are virtually provided to families through reports that these resources are kept for future reference even if they are not actively used. Furthermore, these findings may also be applicable for clinicians to consider when providing families with appropriate mental health resources. That is, the reported acceptance of eMental health resources may support the provision of more eMental health resources as opposed to in-person resources for families with youth experiencing mental health challenges. Additionally, the appreciation by parents in having resources available to them if they are needed suggests that the provision of virtual resources for families with children experiencing sub-threshold symptoms may be beneficial.

Limitations and Future Directions

Notably, a number of limitations were recognized in this study. Firstly, the findings indicating that participants were not familiar with the role of the AI programming may suggest additional discussion is necessary within informed consent procedures that involve telehealth and AI functions. That is, the different types and uses of AI, such as machine learning algorithms, may require targeted in-depth discussion with researchers beyond the layman explanation within consent forms to ensure participant understanding. Notably, the interviews were conducted a year following the original study when parents received their child’s profile and matched eMental health resources. While all interviews were conducted following parent completion of the initial study, the length of time between it ending and the interview occurring may have resulted in distorted memories or forgotten details regarding their associated perceptions and experiences. The forgotten details may include those surrounding the consent process and information provided to participants; therefore, participants may have fully understood the role of AI in the study at the time of consent. Furthermore, the sample was recruited entirely from the participant pool from a previous study; it is possible that this sample is biased, and future research should include a broader parent population. Our sample did not include equal representation across completion rates (e.g., T1 vs. T2 vs. T3). It is possible that participants may have different experiences depending on their level of completion and interaction with the AI matching process and eMental health resource. Future research should therefore prioritize comparing experiences of participants who have different levels of interaction with eMental health resources and AI functions. Additionally, approximately 65% (n = 187) of the invited participants declined to participate in the current study; reasons behind participant decisions were not recorded. It is therefore possible that a degree of selection bias may have impacted our findings and decreased their generalizability given it is unclear why these participants chose not to participate in the follow-up interview study. Further, a large portion of the sample were matched to the reference resource group (73.91%), indicating non-significant internalizing or externalizing mental health concerns and therefore were provided more general mental health online resources as opposed to structured programs. The opinions and perceptions of families of children experiencing more significant mental health challenges may differ and should therefore be explored. Further, a small portion of the sample identified co-occurring conditions (e.g., autism) that their child was diagnosed with; their experiences may further differ from parents of neurotypical children. Finally, as with many studies on mental health topics, these findings are based predominantly on the perceptions of the child’s mother; future studies should seek out the perspectives of other caregivers (e.g., fathers) to ensure a thorough understanding is obtained. Future research should replicate this study with more diverse samples as the majority of our participants were White, well-educated and middle-class.

Conclusion

Increased rates of mental health challenges globally in youth highlight the critical need to better understand factors impacting the accessibility and uptake of mental health resources (Peters-Corbett et al., 2024). Findings provide novel insight into parent perceptions and attitudes towards eMental health resources for their child, and the integration of AI in generating profiles and matching them to suitable and appropriate resources. Noted limitations further direct subsequent research to better understand the experiences of participating families in order to adapt existing, and develop subsequent, accessible and appropriate eMental health resources.

Footnotes

ORCID iDs

Laura de la Roche

Anett Schumacher

Ethical Considerations

This study was approved by Queen’s University Research Ethics Board and Sick Kids Research Hospital and was conducted in accordance with the ethical standards as outlined by the 1964 Declaration of Helsinki and its later amendments. All participants provided written informed consent prior to taking part in the study.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biographies

Dr. Laura de la Roche holds a PhD in developmental psychology from Queen’s University. Her research focuses on understanding the lived experiences of individuals with mental health challenges and the appropriateness and uptake of mental health resources and interventions. She further investigates the lived experiences of families with neurotypical and neurodiverse children with mental health challenges.

Dr. Daphne Korczak is the SickKids’ Chair in Child and Youth Medical Psychiatry, Director of the Children’s Integrated Mood and Body (CLIMB) Depression Program, a Child and Adolescent Psychiatrist, Associate Scientist at the SickKids Research Institute, and an Associate Professor in the Department of Psychiatry at the University of Toronto. Her research focuses on depression and suicide prevention among children and adolescents, and the association of adolescent-onset depression with premature cardiovascular disease. Dr. Korczak leads a large collaborative initiative examining the impact of the pandemic on the mental health of children with and without pre-existing mental health conditions.

Dr. Alice Charach is an Associate Professor, Division of Child and Youth Mental Health, University of Toronto Department of Psychiatry, and Medical Director of the Child and Youth Acute Care Collaborative (CAYACC) in Toronto. She specializes in assessment and treatment of children and youth with attention, learning and behavior difficulties.

Dr. Catherine S. Birken is a general paediatrician in the Division of Paediatric Medicine, Professor at the University of Toronto (U of T), and a Senior Scientist in Child Health Evaluative Sciences at SickKids Research Institute. She is a paediatrician in inpatient paediatrics and a paediatrician in the Healthy Living Clinic for children with complex obesity.

Dr. Kimberley Tsujimoto holds a PhD in Developmental Psychology and Education from the Ontario Institute of Studies in Education (OISE), University of Toronto. She is part of a multidisciplinary research team involved in a large-scale project investigating the impact of the COVID-19 pandemic on child and youth mental health, including the disruption to education.

Dr. Jennifer Crosbie is a Clinical Psychologist and Health Clinician Scientist within the Department of Psychiatry. Dr. Crosbie is an Associate Scientist in the Neurosciences & Mental Health program at SickKids Research Institute and Associate Professor at the University of Toronto, Department of Psychiatry. Dr. Crosbie’s research and clinical work focus on understanding the neurobiological determinants of childhood neuropsychiatric disorders, with a particular focus on attention-deficit/ hyperactivity disorder (ADHD).

Dr. Katherine Cost holds a PhD in neuroscience from Tulane University and is an assistant professor at the University of Waterloo. Her current research focuses on child developmental outcomes within the socio-ecological model framework. Individual factors such as child health behaviours, child temperament, and child sex assigned at birth; proximal contexts such as household income and family functioning; and distal ones such as parental employment quality and neighbourhood quality which have been shaped by systemic racism and classism are all known to impact child developmental outcomes.

Dr. Anett Schumacher received her PhD in Neuroscience from the Universität zu Lübeck in Germany. She is a behavioural neuroscientist who focuses on interactions between behavioural and molecular processes in the brain. She is a research associate working with a multidisciplinary team on a project investigating the impact of the COVID-19 pandemic on the mental health of children and youth at Sick Kids Research Hospital.

Dr. Evdokia Anagnostou is the vice president of research and director at the Bloorview Research Institute and a child neurologist and senior clinician scientist at Holland Bloorview Kids Rehabilitation Hospital. Dr. Anagnostou’s research focuses on translating genomic and systems biology findings into novel treatments for autism spectrum disorder.

Dr. Suneeta Monga is a child & adolescent psychiatrist with a program of clinical research studying pediatric anxiety disorders and depression. She has established an evidence-base for the treatment of preschool anxiety disorders through the creation and evaluation of the Taming Sneaky Fears Program, an innovative manualized cognitive behavior therapy (CBT) program developed specifically for 4- to 7-year-old children with various anxiety disorders including selective mutism, and their parents.

Dr. Elizabeth Kelley holds a PhD in psychology from the University of Chicago. Dr. Kelley is an associate professor at Queen’s University. She has a wide variety of interests in the field of autism research and her research ranges from depression in autistic adolescents to interventions for autistic toddlers to autistic people’s unique strengths.

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eMental Health Resources for Youth: Parent Perceptions of AI Matched eMental Health Resources for Their Child

Abstract

Plain Language Summary

Keywords

The Current Study

Methods

Study Background

Participants

Procedure

Measures

Demographic Questionnaire

Interview

Data Analysis

Trustworthiness

Findings

Theme 1: Feelings regarding AI Integration to Support the Development of Mental Health Profiles and Resource Assignment

Subtheme 1: Positive Feelings Surrounding AI

Subtheme 2: Concerns Surrounding the Use of AI When Assigning and Providing eMental Health Resources to Youth

Theme 2: Parent Perceptions and Uptake of eMental Health Resources

Discussion

Limitations and Future Directions

Conclusion

Footnotes

ORCID iDs

Ethical Considerations

Funding

Declaration of Conflicting Interests

Author Biographies

References