Knowing How to Ask About Digital Culture in Youth Mental Health Care: A Co-Designed Tool.

Introduction

In Canada, young people aged 16–25 years and ‘older youth’ aged 26–35 years spend the most time on digital media compared to other age groups (Government of Canada, S. C2024; MHRC2024, 19), and they are the first to uptake new technologies such as chatbots, TikTok, and virtual reality (Buckle2019; Ipsos2024; Made in CA2024). Understanding the impacts of digital technologies at that age is important because most (75%) mental health problems emerge by age 35 (Solmi et al.2022), and intervening on social and lifestyle factors can prevent or mitigate mental health problems (Salazar de Pablo et al.2021). Indeed, the evolving landscape of artificial intelligence, social media, and video games raises widespread concerns about the impacts of technologies on youth mental health: for example, artificial intelligence chatbots like ChatGPT and Character.AI are thought to have contributed to instances of delusional thinking and suicidal behaviours (Chung et al.2026); social media use is associated with negative social comparisons, inattention, impaired sleep, and loneliness (Capraro et al.2025); and gaming disorder is recognised by the World Health Organization as a persistent pattern of video gaming that is hard to control and persists despite negative psychosocial consequences (WHO2024). Thus, exposure to these and other digital media experiences constitutes potential risk factors for mental health problems among adolescents and young adults.

Like many technologies, however, digital technologies are not just associated with harms but also present several potential benefits. In some circumstances, interaction with artificial intelligence chatbots can mitigate loneliness (Fang et al.2025) and provide meaningful support (Luo et al.2025). Social media allows people to maintain relationships across distances and access safe spaces for self‐expression and communication (Popat and Tarrant2023). Video games can have restorative effects on mood and help players fulfil their needs for autonomy, competence, and social connectedness (Ballou et al.2025). The distinct features of online communication, such as searchability, asynchronicity, and anonymity, enable alternative means for people to express their difficulties, find like‐minded peers, and obtain information about diagnoses and treatments (Rains2020). Importantly, the uses and impacts of digital technologies vary as a function of individual and social contexts. People from LGBTQ+ communities (Berger et al.2022) and people with disabilities (Rains2018), for example, may particularly benefit from online communication for finding safe spaces for self‐expression and peer support. On the other hand, being from a minority or marginalised group puts one at greater risk of harassment and discrimination in online spaces (Imperato and Mancini2025).

Despite widespread concerns for digital‐related risks, mental health professionals feel ill‐equipped to address them. In a sample of 113 psychiatry residents in the United Kingdom, 50.4% observed digital‐related mental health issues in their patients, but the majority (67.3%) did not feel competent to assess these issues (Aref‐Adib et al.2020). This sentiment is shared by psychiatrists, family physicians, and other youth workers (Derges et al.2023; Million et al.2025; Rifkin‐Zybutz et al.2023). Although there are clinical tools and guidelines that can help clinicians assess screen time limits (Tremblay et al.2016) and diagnose gaming disorder (WHO2024), these resources only capture a small part of digital media experiences. Other guides for clinical interviews propose broader assessments (Carson et al.2018; Moreno et al.2024); however, they were developed based on expert opinion without formally consulting young people, making it unclear to what extent they reflect the experiences and priorities of youth. Indeed, when youth are asked by clinicians about their use of digital technologies, most (58%) feel judged or misunderstood (Rifkin‐Zybutz et al.2023). A sensitive approach to this assessment, reflecting the experiences and priorities of young people, holds the potential to improve the patient‐clinician alliance and help intervene on digital‐related risk and protective factors (Jarvis et al.2020; Paquin et al.[in press](#ref-in press)).

To facilitate this, the current project aimed to develop an interview tool for the clinical assessment of digital media experiences in mental health care. We co‐designed the tool with people aged 16–35 years and followed in outpatient mental health clinics to reflect their priorities and experiences. This article reports on the co‐design process and its outcomes: key topics and guidance for the assessment of digital media experiences in mental health care, and the integration of these insights into a novel interview tool—the Digital Culture Interview (DCI).

Theoretical Framework

This project was guided by a cultural psychiatry framework, which recognises the influence of sociocultural factors related to identity, illness understanding (e.g., how mental distress is explained), sources of stress and resilience (e.g., bullying and community support), and the effect of the patient‐clinician relationship (e.g., fear of stigma) on clinical presentation and treatment (Aggarwal and Lewis‐Fernández2015; Jarvis and Kirmayer2021; Kleinman1981). This lens encourages clinicians and researchers to understand people's experiences on their own terms, and as such we used a participatory co‐design approach to develop the Digital Culture Interview (Hagen et al.2012). To capture how the varied impacts of digital technologies go beyond individual behaviours, we employ the concept of digital culture. Digital culture refers not only to platforms, but also to the shared practices, social norms, and expectations—such as the pressure to be available online, conform to the latest trends, and maintain a successful online image—that shape how people engage with digital media (Thumim2012). Digital culture also includes subcultural elements, such as intimate relationships with artificial intelligence agents, online self‐diagnosis promoted by algorithms, conspiracy beliefs, and influencers that shape identity, social ties, and sources of stress and resilience (Paquin2025). The concept is different from digital literacy, which describes competence in using and critically thinking about digital media (Buckingham2015), whereas digital culture centers on meaning‐making and lived experience.

Methods

Study Design

This co‐design initiative, which took place between April and September 2025, involved a combination of group and individual discussions. The full protocol and materials have been published elsewhere (Paquin et al.2025). Reflecting the aim of the DCI and the context in which its use is intended, we sought co‐design participants with lived experience of mental health problems and of undergoing clinical assessments. To be eligible, participants had to be 16–35 years old and followed in outpatient mental health clinics in Montreal, Canada. Participants were also required to speak English and French, reflecting common bilingualism (58.5% of the population) in Montreal (Government of Canada2022). We chose to form a bilingual group to avoid privileging one language over the other and to mirror the cultural composition of the city, with the caveat that populations who are less likely to be bilingual (e.g., recent immigrants) may have been underrepresented. Other eligibility criteria were clinical stability according to the treating team, reported usage of digital technologies (social media, online videos, or video games) at least multiple times a week, and lack of intellectual disability. The study received ethical approval from the Research Ethics Board of the CIUSSS de l'Ouest‐de‐l'Île‐de‐Montréal (MP‐18‐2025‐1164), and all participants provided written informed consent.

Participants were recruited using a purposive sampling strategy, aiming for diversity across genders and diagnostic categories (including but not restricted to psychotic, affective, neurodevelopmental, and personality disorders). We contacted mental health professionals and joined their team meetings to solicit their help in presenting the study to patients who may be interested. We additionally displayed flyers inviting interested individuals to contact us directly. All participants were met with individually to present the study objectives and review the consent form. Our goal was to recruit approximately 10 participants—a number large enough to provide a diversity of perspectives, but small enough to allow group discussions. We approached 21 people, of whom 5 were not interested and 4 could not be reached, leading to a final group of 12 (Table1).

Table: Descriptive information about co‐design participants (N= 12).

Positionality

We recognise that researchers' backgrounds and social positions influence research, including interactions with participants, power imbalances, and the interpretation of data. The first author and lead researcher on the project, VP, is a psychiatrist in his 30s, White settler, born to a middle‐class French‐Canadian family. His research, guided by a critical realist paradigm (Paquin2025), focuses on digital culture and youth mental health. The second author, RJ, co‐moderated the co‐design meetings. She is a medical student in her 20s, born to a middle‐class immigrant Chinese family, whose interest in digital culture and mental health research is guided by her experience as a health and lifestyle content creator. Other team members, representing various backgrounds and disciplines—cultural psychiatry, digital mental health, youth mental health, psychology, ethnography, and occupational therapy, as well as various forms of engagement with digital cultures—contributed to the development of the protocol and the interpretation of findings.

Co‐Design Process

The co‐design process was structured around two group meetings. The first meeting employed the nominal group technique to generate ideas for the content of the DCI. This technique consists in inviting participants to write down their ideas silently, then present their ideas one at a time in turn, until all ideas are presented (Harvey and Holmes2012). At the end, participants vote on the ideas they find most important or relevant for the project. The advantage of the nominal group technique, relative to a focus group, is that it ensures equal opportunities for participation. To guide the generation of ideas, we instructed participants to think about the beneficial and harmful effects of digital media practices, as well as the impact that digital media can have on identity, understandings of mental illness and well‐being, and the patient‐clinician relationship.

Based on the ideas deemed most relevant by participants, our research team produced a first draft of the DCI. We shared it with participants by email, then revised it with participants during the second group meeting. Participants were invited to remove, revise, or add elements, and to provide feedback on the co‐design process. Throughout the co‐design process, participants unable to attend the group meetings were invited to individual interviews.

Analysis

We conducted a rapid thematic analysis (Braun and Clarke2012) after the initial nominal group discussion to synthesise participants' answers into a list of (micro‐)themes that participants could subsequently rank by importance. This analysis was conducted by VP and reviewed with RJ using word processor software. VP began by listening to recordings of the nominal group discussions and revising the automatic transcriptions generated by Microsoft Teams. He inductively coded all responses from participants, attributing one or multiple codes per response, then extracted all codes and generated themes. VP and RJ revised the themes together and organised them by categories for presentation to participants. A second thematic analysis was conducted by VP and RJ at the end of the co‐design process to inductively identify themes related to two pre‐specified categories: participants' perspective on how the DCI should be administered, and the anticipated impacts of using the DCI in clinical practice.

Results

Generation of Themes

The first group meeting was 1.5 h. Of 12 participants, two were unable to attend and were met with individually, including one in‐person meeting at the clinic (all other interviews were virtual). A third participant could not attend but provided feedback by email, leaving nine participants in the group. We had initially planned to collect participants' votes during this first group meeting; however, due to time constraints and because some participants were absent, we opted for soliciting participants' votes by email after the meeting. This also allowed us to conduct a rapid thematic analysis to more thoroughly synthesise their answers.

A total of 48 themes were identified, which we organised into eight categories: online communities (eight themes), distraction and coping (four themes), online safety (six themes), algorithms and misinformation (five themes), problematic use (eight themes), social pressures (three themes), mental health information (eight themes), and the patient‐clinician relationship (six themes). The full list of themes is in TableS1.

This list was sent to participants by email, and participants were instructed to rank, in order, the five themes they found most relevant to explore in an initial clinical encounter. We converted their rankings into scores, so that a first rank weighs five points, a second rank weighs four points, and so on, which we then summed across participants to identify the highest‐ranked themes. As a starting point, we selected the top 15 themes, which all had a score of five points or more. We chose this number because we were aiming for an interview tool that takes about 30 min to administer and estimated that 15 themes would amount to about 15 questions or 30 min. Other themes not included in the top 15 were later reviewed with participants to consider additional elements for inclusion in the DCI.

Drafting the Interview

As a first draft of the DCI, VP prepared questions covering the 15 themes and organised them into categories. Given some overlap between the 15 themes, this first draft included 11 questions. Participants were sent by email the draft accompanied by the 15 themes that the questions were meant to capture (TableS2). We subsequently held the second group meeting, which lasted 1 h, to solicit their revisions to the draft, including to adjudicate on the transition from themes to DCI items, adjust the wording and structure of the questions, and incorporate additional content as needed based on the initial 48 themes or new insights. Participants could see the draft and list of themes during the meeting through screen sharing, and VP's note‐taking on the draft was visible to all, allowing for member checking. Participants were sent the revised draft again after the meeting to solicit further feedback. Seven attended the group meeting, two were unable to attend and were met with individually instead, one left the study, and two others could not be reached.

Participants made several suggestions, leading to extensive changes to the wording of all questions, elaboration on some of the questions, the merging of two questions, and the addition of four questions. Participants emphasised the importance of distinguishing between digital content that the patient may beintentionallyseeking out on the internet from content that ispresentedto them by algorithms. They suggested asking not only about upsetting content seen online, but also inspiring content. In a question about being targeted or bullied online, they noted that the word “bullied” can carry a strong emotional connotation, and they advised asking instead about ‘people online who are treating you poorly’ as a more inclusive phrasing that might be less likely to cause distress. Participants recommended the addition of a question about the pressure to conform to online trends and norms. They also noted that it would be helpful to contrast how a person expresses their identity and copes with challenges online compared to offline. To better explore the impacts of scrolling, which could include negative emotional experiences as well as fatigue and attentional impairments, participants suggested inquiring about effects that are felt ‘physically, emotionally, or mentally’. After those interviews with participants, we invited clinicians and researchers on the team to provide their feedback, leading to final revisions to make the questions more open‐ended and to reorder them for flow. The resulting interview tool includes 14 questions (Table2). Exemplars based on the themes generated by participants are included alongside the interview questions to provide further guidance. The DCI is a living toolkit, and eventual revisions will be made available via the Open Science Framework:https://osf.io/3g7zc/.

Table: The Digital Culture Interview.

Guidance for Administering the Interview

Beyond the questions that compose the DCI, participants voiced suggestions and concerns regarding how the questions should and should not be used. Their guidance, detailed below, was embedded directly in the interview toolkit, alongside the interview questions and exemplars.

Participants highlighted that youth digital culture is a complex topic that requires a sensitive exploration. The DCI should be administered flexibly by clinicians, adapting its length and using follow‐up questions as needed to further explore patients' experiences. Some questions may need to be revisited beyond the initial interview to allow trust to develop between the patient and the clinician. The person may anticipate being judged or facing negative repercussions for disclosing their digital media experiences and practices:

Reflecting on marginalised or unusual contents that young people may seek online, many participants insisted on the importance of a non‐judgemental, open‐minded exploration:

It was underscored that clinicians should not jump to conclusions about the person's unusual interests or habits involving digital technologies. Clinicians must be wary of being influenced by societal stigma surrounding some digital practices, such as young people's use of artificial intelligence chatbots as social companions and for psychological support.

Participants noted that clinicians often lack knowledge about young people's online spaces and practices. Consequently, clinicians may not have a good understanding of what patients are disclosing in response to the DCI's questions. In turn, a lack of understanding of patients' digital preferences and habits may lead to difficulties in differentiating problematic and non‐problematic behaviours, with the risk of harmless activities being pathologised by clinicians.

A patient's openness about their digital media practices will depend on their relationship with the clinician, including how long they have been working together and whether the clinician is deemed trustworthy. One of the participants suggested that, to foster a better understanding of patients' experiences, clinicians should familiarise themselves with online spaces and acquire a baseline of knowledge. In addition, clinicians can build trust and reciprocate these disclosures by sharing digital content that is relevant to what the person is discussing: for example, if the person mentions ameme(an internet‐based humoristic image), the clinician can show another one in return. Importantly, clinicians should recognise when the patient is not comfortable discussing a particular topic and allow them not to answer. At the same time, clinicians should not forgo delicate questions: asking directly about topics such as online self‐diagnosis, where patients may assume clinicians' disapproval, is necessary to open a space for discussion.

Anticipated Impacts of the Digital Culture Interview

Participants stressed the potential impact that improving clinicians' awareness of digital culture might have on the patient‐clinician alliance:

They observed that some questions in particular, such as those about digital contents sought online and online self‐diagnosis, might open conversations about topics that are meaningful to the patients yet often unexplored in clinical care. One participant noted the questions' broad relevance for understanding young people's experiences with digital technologies but was uncertain as to how the clinical exploration of these topics might influence clinical management. Overall, participants perceived the DCI as potentially impactful, but also potentially intrusive, requiring a careful use by the clinician.

Discussion

Through a combination of group discussions, individual interviews, and email exchanges, we developed with young people a clinical interview tool addressing the role of digital cultural factors in mental health. To our knowledge, this is the first interview tool designed with youth to explore the impacts of digital technologies on mental health in clinical care, beyond a single technology (e.g., social media) or metric (e.g., screen time). Drawing on a sociocultural perspective, its content reflects the experiences and knowledge of a diverse group of young people receiving mental health services. The hope is that by using the DCI, clinicians may better understand the digital lived experiences of youth, enhance their alliance with patients and better identify a person's unique risk and protective factors for mental health.

Participants emphasised that patients may worry about being stigmatised or misunderstood for their digital practices and preferences, echoing the experiences of young people reported elsewhere (Plackett et al.2026; Rifkin‐Zybutz et al.2023). Indeed, internet‐related practices are often framed negatively in popular discourses, particularly when it comes to youth culture (Orben2020), and the stigma is even greater for internet subcultural communities that diverge from the mainstream (Paquin et al.2026). To support a non‐judgemental exploration of the patient's experiences with digital technologies, the DCI includes questions not only about negative experiences, but also positive ones, such as coping and online support, and provides explicit instructions to the clinician (e.g., ‘Keep an open mind: some digital practices may seem unusual but are not necessarily harmful’; Table2). However, this content does not fully address young people's fear of being misunderstood. To manage sensitive disclosures, clinicians may consider employing normalizing statements (e.g., ‘Some people see AI as a tool, while others develop a deep attachment to their chatbot; how is it like for you?’), open‐ended inquiries about motivations and positive experiences (e.g., ‘What do you like about interacting with this application?’), and validation (e.g., ‘I can understand how this interaction has helped you go through a difficult period’) (Shea2016). More broadly, we believe that the implementation and use of the DCI should be accompanied by training to better address preconceptions among clinicians that are potentially stigmatizing or pathologizing.

The DCI's holistic exploration of positive and negative experiences is consistent with the view that an assessment of digital technology use should not be exclusively focused on problematic use or screen time, and that it should also be open to exploring digital experiences that are beneficial for a person's well‐being (Hamati et al.2025; Looi et al.2024; Moreno et al.2024). Reflecting important threads of the literature on digital media effects, the DCI addresses the role of digital culture in identity development (Granic et al.2020), negative social comparisons online (Gill2023; van der Wal et al.2024), harmful contents promoted by algorithms (Brady et al.2023; Hamilton et al.2025), and the addictive use of digital technologies (Brand et al.2025). It explores interpersonal factors, including the use of digital technologies for creating and maintaining supportive social connections, which can be especially important for coping with illness and marginalisation (Berger et al.2022; Rains2018). Interpersonal stressors, such as the exposure to discrimination and harassment online (Nickerson and Fredrick2025), as well as the displacement of in‐person interactions in favour of screen time (Capraro et al.2025), are also captured by the interview questions and exemplars. Finally, the DCI's open‐ended questions may support the exploration of newer, rapidly evolving phenomena, such as the use of artificial intelligence companions for coping with mental health problems and loneliness (Laestadius et al.2024; Luo et al.2025), as well as shifting representations of diagnoses and treatments on social media (Alper et al.2025).

The DCI rests on a concept of digital culture that ties together social media, video games, artificial intelligence, and other technologies and media forms. Although generally distinct, these technologies participate in a broader media ecology (Scolari2012) and sometimes operate through shared pathways: online relationships can be formed in video games and exported to social media; smartphones, streaming, gaming, and social media can be implicated in forms of problematic (addictive) use that interferes with functioning (Brand et al.2025); and a person's affinity for chatbots or influencers may draw them to online communities who share their interest (Paquin et al.2026). Other affordances are more technology‐specific, such as social comparisons on social media (Orben et al.2024), artificial sociality with chatbots (Depounti and Natale2025), and interactive rewards in video games (Flayelle et al.2023). Clinicians should consider both common and specific mechanisms for identifying the mental health risks and benefits of digital practices and their implications for intervention.

Overall, these themes represent factors that can act as predisposing, precipitating, perpetuating, or protective factors in a clinical presentation. By supporting their personalised evaluation in clinical care, the DCI may orient intervention approaches that mitigate harmful exposures while promoting and reinforcing the patient's resources. For example, a person's deep involvement in video games could indicate an affinity for tabletop roleplay games, which can be used as psychotherapeutic devices (Billieux et al.2025; Henrich and Worthington2023). Someone's difficulty with controlling their use of social media may contribute to depressive symptoms and interfere with plans for behavioural activation, prompting a tailored intervention to limit usage (Plackett et al.2023). Another person's close relationship with a social chatbot could be a source of concern yet reveal an effective strategy for improving social skills (Franze et al.2023). And as participants highlighted, if clinicians do not ask, patients may not tell.

A key limitation is that, owing to the geographical focus, the recruitment strategy and the small size of the co‐design group, the experiences and priorities identified in this project cannot reflect those of all youth. Populations in low‐income countries, recent immigrants, and youth with intellectual disability, for example, were not represented and may have varying practices and views related to digital technologies. A person's access to and engagement with digital culture is shaped by their languages (including whether they speak local majority languages), the prescription of specific digital practices within cultural communities (e.g., using or abstaining from using technologies for religion; Campbell and Tsuria2021), and patterns of digital help‐seeking and literacy related to one's psychosocial needs and (dis)abilities. Accordingly, following the participants' call for a personalised administration of the DCI, some questions may be more or less relevant depending on a patient's unique context, and we encourage an intersectional approach to digital culture that accounts for the person's identities and sociocultural background. Another consideration is the influence of our positionality on the co‐design process. A potential strength is that the discussions were moderated by two people close in age to the study participants, with hands‐on knowledge of online spaces popular among youth. This background provided some degree of insider's perspective and may have facilitated rapport, though it also required the researchers to bracket their assumptions when listening to and interpreting participants' answers. In addition, given the first author's position as researcher and physician, participants might have prioritised discourses on technologies that they deemed scientifically and socially acceptable, to the detriment of more controversial views. Marginalised topics such as pornography use and the Dark Web, for instance, were not discussed. However, participants did criticise mainstream clinical practices, and they emphasised the risk of stigma towards youth digital practices. The next step of this project will be to test the DCI with an independent sample of people followed in outpatient mental health clinics and train clinicians in evaluating the digital lived experiences of their patients. We will collect feedback from patients and clinicians to explore the implications of the DCI for youth mental health care and examine its potential for implementation in clinical practice (Paquin et al.2025).

Conclusion

This article presented the development of the DCI, an interview tool co‐designed with young people to support the assessment of digital media experiences and practices in clinical care. The interview guide covers a range of positive and negative experiences related to digital technologies and emphasises the importance of a non‐judgemental, flexible approach to clinical assessment. Potential impacts, to be examined in future research, are that the DCI can enhance rapport and support personalised interventions in youth mental health care.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgements

We are grateful to the participants of this project who co‐designed the Digital Culture Interview and provided their perspective on the exploration of digital culture in youth mental health care. R.J. was supported by an award from the Mach‐Gaensslen Foundation and the Dr. Clarke K. McLeod Memorial Scholarship.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Associated Data

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

1. Aggarwal, N. K. , and Lewis‐Fernández R.. 2015. “An Introduction to the Cultural Formulation Interview.” Focus 13, no. 4: 426–431. 10.1176/appi.focus.20150016. doi.org/10.1176/appi.focus.20150016
2. Alper, M. , Rauchberg J. S., Simpson E., Guberman J., and Feinberg S.. 2025. “TikTok as Algorithmically Mediated Biographical Illumination: Autism, Self‐Discovery, and Platformed Diagnosis on #Autisktok.” New Media & Society 27, no. 3: 1378–1396. 10.1177/14614448231193091. doi.org/10.1177/14614448231193091
3. Aref‐Adib, G. , Landy G., Eskinazi M., et al. 2020. “Assessing Digital Risk in Psychiatric Patients: Mixed Methods Study of Psychiatry Trainees' Experiences, Views, and Understanding.” JMIR Mental Health 7, no. 7: e19008. 10.2196/19008. doi.org/10.2196/19008
4. Ballou, N. , Hakman T., Vuorre M., Magnusson K., and Przybylski A. K.. 2025. “How Do Video Games Affect Mental Health? A Narrative Review of 13 Proposed Mechanisms.” Technology, Mind, and Behavior 6, no. 2: 123–143. 10.1037/tmb0000152. doi.org/10.1037/tmb0000152
5. Berger, M. N. , Taba M., Marino J. L., Lim M. S. C., and Skinner S. R.. 2022. “Social Media Use and Health and Well‐Being of Lesbian, Gay, Bisexual, Transgender, and Queer Youth: Systematic Review.” Journal of Medical Internet Research 24, no. 9: e38449. 10.2196/38449. doi.org/10.2196/38449
6. Billieux, J. , Fournier L., Rochat L., et al. 2025. “Can Playing Dungeons and Dragons Be Good for You? A Registered Exploratory Pilot Programme Using Offline Tabletop Role‐Playing Games to Mitigate Social Anxiety and Reduce Problematic Involvement in Multiplayer Online Video Games.” Royal Society Open Science 12, no. 4: 250273. 10.1098/rsos.250273. doi.org/10.1098/rsos.250273
7. Brady, W. J. , Jackson J. C., Lindström B., and Crockett M. J.. 2023. “Algorithm‐Mediated Social Learning in Online Social Networks.” Trends in Cognitive Sciences 27, no. 10: 947–960. 10.1016/j.tics.2023.06.008. doi.org/10.1016/j.tics.2023.06.008
8. Brand, M. , Antons S., Bőthe B., et al. 2025. “Current Advances in Behavioral Addictions: From Fundamental Research to Clinical Practice.” American Journal of Psychiatry 182, no. 2: 155–163. 10.1176/appi.ajp.20240092. doi.org/10.1176/appi.ajp.20240092
9. Braun, V. , and Clarke V.. 2012. “Thematic Analysis.” In APA Handbook of Research Methods in Psychology, Vol 2: Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological, edited by Cooper H., Camic P. M., Long D. L., Panter A. T., Rindskopf D., and Sher K. J., 57–71. American Psychological Association. 10.1037/13620-004. doi.org/10.1037/13620-004
10. Buckingham, D. 2015. “Defining Digital Literacy—What Do Young People Need to Know About Digital Media?” Nordic Journal of Digital Literacy 10, no. Jubileumsnummer: 21–35. 10.18261/ISSN1891-943X-2015-Jubileumsnummer-03. doi.org/10.18261/ISSN1891-943X-2015-Jubileumsnummer-03
11. Buckle, C. 2019. “AR vs VR: The Challenges and Opportunities in 2019.”https://www.gwi.com/blog/augmented‐virtual‐reality.
12. Campbell, H. A. , and Tsuria R., eds. 2021. Digital Religion: Understanding Religious Practice in Digital Media. 2nd ed. Routledge. 10.4324/9780429295683. doi.org/10.4324/9780429295683
13. Capraro, V. , Globig L., Rausch Z., et al. 2025. A Collective Review on Some Potential Negative Impacts of Smartphone and Social Media Use on Adolescent Mental Health: Results From a Delphi Process [SSRN Scholarly Paper]. Social Science Research Network. 10.2139/ssrn.5256747. doi.org/10.2139/ssrn.5256747
14. Carson, N. J. , Gansner M., and Khang J.. 2018. “Assessment of Digital Media Use in the Adolescent Psychiatric Evaluation.” Child and Adolescent Psychiatric Clinics of North America 27, no. 2: 133–143. 10.1016/j.chc.2017.11.003. doi.org/10.1016/j.chc.2017.11.003
15. Chung, V.‐H.‐A. , Bernier P., and Hudon A.. 2026. “Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review.” JMIR Mental Health 13, no. 1: e93040. 10.2196/93040. doi.org/10.2196/93040
16. Depounti, I. , and Natale S.. 2025. “Decoding Artificial Sociality: Technologies, Dynamics, Implications.” New Media & Society 27, no. 10: 5457–5470. 10.1177/14614448251359217. doi.org/10.1177/14614448251359217
17. Derges, J. , Bould H., Gooberman‐Hill R., et al. 2023. “Mental Health Practitioners' and Young People's Experiences of Talking About Social Media During Mental Health Consultations: Qualitative Focus Group and Interview Study.” JMIR Formative Research 7: e43115. 10.2196/43115. doi.org/10.2196/43115
18. Fang, C. M. , Liu A. R., Danry V., et al. 2025. “How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Randomized Controlled Study.” arXiv. 10.48550/arXiv.2503.17473. doi.org/10.48550/arXiv.2503.17473
19. Flayelle, M. , Brevers D., King D. L., Maurage P., Perales J. C., and Billieux J.. 2023. “A Taxonomy of Technology Design Features That Promote Potentially Addictive Online Behaviours.” Nature Reviews Psychology 2, no. 3: 136–150. 10.1038/s44159-023-00153-4. doi.org/10.1038/s44159-023-00153-4
20. Franze, A. , Galanis C. R., and King D. L.. 2023. “Social Chatbot Use (e.g., ChatGPT) Among Individuals With Social Deficits: Risks and Opportunities.” Journal of Behavioral Addictions 12, no. 4: 871–872. 10.1556/2006.2023.00057. doi.org/10.1556/2006.2023.00057
21. Gill, R. 2023. Perfect: Feeling Judged on Social Media. John Wiley & Sons.
22. Government of Canada, S. C . 2022. “Focus on Geography Series, 2021 Census—Montréal (Census subdivision).”https://www12.statcan.gc.ca/census‐recensement/2021/as‐sa/fogs‐spg/page.cfm?lang=E&topic=6&dguid=2021A00052466023.
23. Government of Canada, S. C . 2024. “Digital Well‐Being: The Relationship Between Technology Use, Mental Health and Interpersonal Relationships.”https://www150.statcan.gc.ca/n1/pub/22‐20‐0001/222000012024001‐eng.htm.
24. Granic, I. , Morita H., and Scholten H.. 2020. “Beyond Screen Time: Identity Development in the Digital Age.” Psychological Inquiry 31, no. 3: 195–223. 10.1080/1047840X.2020.1820214. doi.org/10.1080/1047840X.2020.1820214
25. Hagen, P. , Collin P., Metcalf A., Nicholas M., Rahilly K., and Swainston N.. 2012. Participatory Design of Evidence‐Based Online Youth Mental Health Promotion, Prevention, Early Intervention and Treatment. Young and Well Cooperative Research Centre.
26. Hamati, F. J. , Chapman J. A., and Sood A.. 2025. “Cracking the Algorithm: How to Ask the Right Questions About Social Media During the Interview.” Pediatric Clinics of North America 72, no. 2: 249–265. 10.1016/j.pcl.2024.08.003. doi.org/10.1016/j.pcl.2024.08.003
27. Hamilton, J. L. , Untawale S., Dalack M. N., Thai A. B., Kleiman E. M., and Yao A.. 2025. “Self‐Harm Content on Social Media and Proximal Risk for Self‐Injurious Thoughts and Behaviors Among Adolescents.” JAACAP Open 3, no. 3: 431–438. 10.1016/j.jaacop.2024.11.008. doi.org/10.1016/j.jaacop.2024.11.008
28. Harvey, N. , and Holmes C. A.. 2012. “Nominal Group Technique: An Effective Method for Obtaining Group Consensus.” International Journal of Nursing Practice 18, no. 2: 188–194. 10.1111/j.1440-172X.2012.02017.x. doi.org/10.1111/j.1440-172X.2012.02017.x
29. Henrich, S. , and Worthington R.. 2023. “Let Your Clients Fight Dragons: A Rapid Evidence Assessment Regarding the Therapeutic Utility of ‘Dungeons & Dragons.’.” Journal of Creativity in Mental Health 18, no. 3: 383–401. 10.1080/15401383.2021.1987367. doi.org/10.1080/15401383.2021.1987367
30. Imperato, C. , and Mancini T.. 2025. “Beyond Bytes: A Meta‐Analytical Examination of the Association Between Online Discrimination and Mental Health and Well‐Being.” Aggression and Violent Behavior 85: 102092. 10.1016/j.avb.2025.102092. doi.org/10.1016/j.avb.2025.102092
31. Ipsos . 2024. “34% of Canadians Believe the Development of AI Technologies is a Bad Thing; 30% Think it's Good.”https://www.ipsos.com/en‐ca/34‐percent‐canadians‐believe‐development‐ai‐technologies‐bad‐thing‐30‐think‐its‐good.
32. Jarvis, G. E. , and Kirmayer L. J.. 2021. “Situating Mental Disorders in Cultural Frames.” Oxford Research Encyclopedias, Psychology 32: 1–32.
33. Jarvis, G. E. , Kirmayer L. J., Gómez‐Carrillo A., Aggarwal N. K., and Lewis‐Fernández R.. 2020. “Update on the Cultural Formulation Interview.” Focus (American Psychiatric Publishing) 18, no. 1: 40–46. 10.1176/appi.focus.20190037. doi.org/10.1176/appi.focus.20190037
34. Kleinman, A. 1981. Patients and Healers in the Context of Culture: An Exploration of the Borderland Between Anthropology, Medicine, and Psychiatry. University of California Press.
35. Laestadius, L. , Bishop A., Gonzalez M., Illenčík D., and Campos‐Castillo C.. 2024. “Too Human and Not Human Enough: A Grounded Theory Analysis of Mental Health Harms From Emotional Dependence on the Social Chatbot Replika.” New Media & Society 26, no. 10: 5923–5941. 10.1177/14614448221142007. doi.org/10.1177/14614448221142007
36. Looi, J. C. , Wilkes F. A., Bastiampillai T., and Allison S.. 2024. “Practical Psychiatry: Taking Gaming Seriously: A Primer for Psychiatrists on Gamers and Gaming Culture.” Australasian Psychiatry 32, no. 1: 59–62. 10.1177/10398562231211137. doi.org/10.1177/10398562231211137
37. Luo, X. , Ghosh S., Tilley J. L., Besada P., Wang J., and Xiang Y.. 2025. “Shaping ChatGPT Into My Digital Therapist: A Thematic Analysis of Social Media Discourse on Using Generative Artificial Intelligence for Mental Health.” Digital Health 11: 20552076251351088. 10.1177/20552076251351088. doi.org/10.1177/20552076251351088
38. Made in CA . 2024. “TikTok Statistics in Canada.”https://madeinca.ca/tiktok‐statistics‐canada/.
39. MHRC . 2024. “Understanding the Mental Health of Canadians Through Covid 19 and Beyond: Poll #19.” Mental Health Research Canada website:https://www.mhrc.ca/findings‐of‐poll‐19.
40. Million, E. , Herbreteau M., Bourrel G., et al. 2025. “The Strange Adolescent World That Complicates General Practitioner Prevention: A Qualitative Study.” BJGP Open 9: BJGPO.2024.0290. 10.3399/BJGPO.2024.0290. doi.org/10.3399/BJGPO.2024.0290
41. Moreno, M. A. , Dixon L. B., Jankowski S., et al. 2024. “The Need to Adapt the Psychiatric Clinical Assessment to the Digital Age: A Practical Approach.” Psychiatric Services 75, no. 9: 929–931. 10.1176/appi.ps.20230399. doi.org/10.1176/appi.ps.20230399
42. Nickerson, A. B. , and Fredrick S. S.. 2025. “Cyberbullying and Mental Health in Children and Adolescents.” Annual Review of Clinical Psychology 22: 559–586. 10.1146/annurev-clinpsy-061324-073435. doi.org/10.1146/annurev-clinpsy-061324-073435
43. Orben, A. 2020. “The Sisyphean Cycle of Technology Panics.” Perspectives on Psychological Science 15, no. 5: 1143–1157. 10.1177/1745691620919372. doi.org/10.1177/1745691620919372
44. Orben, A. , Meier A., Dalgleish T., and Blakemore S.‐J.. 2024. “Mechanisms Linking Social Media Use to Adolescent Mental Health Vulnerability.” Nature Reviews Psychology 3, no. 6: 407–423. 10.1038/s44159-024-00307-y. doi.org/10.1038/s44159-024-00307-y
45. Paquin, V. 2025. “Taking the Long View on Digital Culture and Mental Health: Principles From Critical Realism.” Qualitative Health Research: 10497323251389814. 10.1177/10497323251389814. doi.org/10.1177/10497323251389814
46. Paquin, V. , Aversa S., Nickrenz E., et al. 2025. “Integrating the Digital Culture of Youth in Clinical Mental Health Assessments: Protocol for the Codesign and Pilot Test of an Interview Method.” BMJ Open 15, no. 11: e104200. 10.1136/bmjopen-2025-104200. doi.org/10.1136/bmjopen-2025-104200
47. Paquin, V. , Plante C. N., and Nickrenz E.. 2026. “Cultural Psychiatry Across (Cyber)spaces: Understanding the Role of Fan Cultures in Mental Health.” Transcultural Psychiatry. 10.1177/13634615261435698. doi.org/10.1177/13634615261435698
48. Plackett, R. , Blyth A., and Schartau P.. 2023. “The Impact of Social Media Use Interventions on Mental Well‐Being: Systematic Review.” Journal of Medical Internet Research 25, no. 1: e44922. 10.2196/44922. doi.org/10.2196/44922
49. Plackett, R. , van der Scheer I., Steward J.‐M., et al. 2026. “Exploring the Barriers and Facilitators to Discussing Social Media in Primary Care for Young Adults With Mental Health Concerns: A Qualitative Study.” BJGP Open. BJGPO.2025.0196. 10.3399/BJGPO.2025.0196. doi.org/10.3399/BJGPO.2025.0196
50. Popat, A. , and Tarrant C.. 2023. “Exploring Adolescents' Perspectives on Social Media and Mental Health and Well‐Being—A Qualitative Literature Review.” Clinical Child Psychology and Psychiatry 28, no. 1: 323–337. 10.1177/13591045221092884. doi.org/10.1177/13591045221092884
51. Rains, S. A. 2018. Coping With Illness Digitally. MIT Press. 10.7551/mitpress/11219.001.0001. doi.org/10.7551/mitpress/11219.001.0001
52. Rains, S. A. 2020. “Communication Technology Affordances and Social Support Provision.” In Social Support and Health in the Digital Age, edited by Egbert N. and Wright K. B.. Lexington Books; eBook Collection (EBSCOhost).https://proxy.library.mcgill.ca/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2354555&scope=site.
53. Rifkin‐Zybutz, R. , Turner N., Derges J., et al. 2023. “Digital Technology Use and Mental Health Consultations: Survey of the Views and Experiences of Clinicians and Young People.” JMIR Mental Health 10: e44064. 10.2196/44064. doi.org/10.2196/44064
54. Salazar de Pablo, G. , De Micheli A., Solmi M., et al. 2021. “Universal and Selective Interventions to Prevent Poor Mental Health Outcomes in Young People: Systematic Review and Meta‐Analysis.” Harvard Review of Psychiatry 29, no. 3: 196–215. 10.1097/HRP.0000000000000294. doi.org/10.1097/HRP.0000000000000294
55. Scolari, C. A. 2012. “Media Ecology: Exploring the Metaphor to Expand the Theory.” Communication Theory 22, no. 2: 204–225. 10.1111/j.1468-2885.2012.01404.x. doi.org/10.1111/j.1468-2885.2012.01404.x
56. Shea, S. C. 2016. Psychiatric Interviewing: The Art of Understanding. Elsevier.
57. Solmi, M. , Radua J., Olivola M., et al. 2022. “Age at Onset of Mental Disorders Worlwide: Large‐Scale Meta‐Analysis of Epidemiological Studies.” Molecular Psychiatry 27: 281–295. 10.17863/CAM.69315. doi.org/10.17863/CAM.69315
58. Thumim, N. 2012. Self‐Representation and Digital Culture. Palgrave Macmillan UK. 10.1057/9781137265135. doi.org/10.1057/9781137265135
59. Tremblay, M. S. , Carson V., Chaput J.‐P., et al. 2016. “Canadian 24‐Hour Movement Guidelines for Children and Youth: An Integration of Physical Activity, Sedentary Behaviour, and Sleep.” Applied Physiology, Nutrition, and Metabolism 41, no. 6 (Suppl. 3): S311–S327. 10.1139/apnm-2016-0151. doi.org/10.1139/apnm-2016-0151
60. van der Wal, A. , Valkenburg P. M., and van Driel I. I.. 2024. “In Their Own Words: How Adolescents Use Social Media and How It Affects Them.” Social Media + Society 10, no. 2: 20563051241248591. 10.1177/20563051241248591. doi.org/10.1177/20563051241248591
61. WHO . 2024. “Clinical Descriptions and Diagnostic Requirements for ICD‐11 Mental, Behavioural and Neurodevelopmental Disorders (CDDR).”https://www.who.int/publications/i/item/9789240077263.

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Republished from the open web under CC-BY. Authors: Paquin V, Jiang R, Shah JL, Jarvis GE, Nickrenz E, Ferrari M, Park M. Read the original.