Ownership, Use of, and Interest in Digital Mental Health Technologies Among Clinicians and Young People Across a Spectrum of Clinical Care Needs: Cross-sectional Survey
Bibliographic record
Abstract
Background There is currently an increased interest in and acceptance of technology-enabled mental health care. To adequately harness this opportunity, it is critical that the design and development of digital mental health technologies be informed by the needs and preferences of end users. Despite young people and clinicians being the predominant users of such technologies, few studies have examined their perspectives on different digital mental health technologies. Objective This study aims to understand the technologies that young people have access to and use in their everyday lives and what applications of these technologies they are interested in to support their mental health. The study also explores the technologies that youth mental health clinicians currently use within their practice and what applications of these technologies they are interested in to support their clients’ mental health. Methods Youth mental health service users (aged 12-25 years) from both primary and specialist services, young people from the general population (aged 16-25 years), and youth mental health clinicians completed a web-based survey exploring technology ownership, use of, and interest levels in using different digital interventions to support their mental health or that of their clients. Results A total of 588 young people and 73 youth mental health clinicians completed the survey. Smartphone ownership or private access among young people within mental health services and the general population was universal (611/617, 99%), with high levels of access to computers and social media. Youth technology use was frequent, with 63.3% (387/611) using smartphones several times an hour. Clinicians reported using smartphones (61/76, 80%) and video chat (69/76, 91%) commonly in clinical practice and found them to be helpful. Approximately 50% (296/609) of the young people used mental health apps, which was significantly less than the clinicians (χ23=28.8, n=670; P<.001). Similarly, clinicians were significantly more interested in using technology for mental health support than young people (H3=55.90; P<.001), with 100% (73/73) of clinicians being at least slightly interested in technology to support mental health compared with 88% (520/591) of young people. Follow-up tests revealed no difference in interest between young people from the general population, primary mental health services, and specialist mental health services (all P>.23). Young people were most interested in web-based self-help, mobile self-help, and blended therapy. Conclusions Technology access is pervasive among young people within and outside of youth mental health services; clinicians are already using technology to support clinical care, and there is widespread interest in digital mental health technologies among these groups of end users. These findings provide important insights into the perspectives of young people and clinicians regarding the value of digital mental health interventions in supporting youth mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".