Examining the Feasibility of Implementing Digital Mental Health Innovations Into Hospitals to Support Youth in Suicide Crisis: Interview Study With Young People and Health Professionals
Bibliographic record
Abstract
BACKGROUND: Hospitals are insufficiently resourced to appropriately support young people who present with suicidal crises. Digital mental health innovations have the potential to provide cost-effective models of care to address this service gap and improve care experiences for young people. However, little is currently known about whether digital innovations are feasible to integrate into complex hospital settings or how they should be introduced for sustainability. OBJECTIVE: This qualitative study explored the potential benefits, barriers, and collective action required for integrating digital therapeutics for the management of suicidal distress in youth into routine hospital practice. Addressing these knowledge gaps is a critical first step in designing digital innovations and implementation strategies that enable uptake and integration. METHODS: We conducted a series of semistructured interviews with young people who had presented to an Australian hospital for a suicide crisis in the previous 12 months and hospital staff who interacted with these young people. Participants were recruited from the community nationally via social media advertisements on the web. Interviews were conducted individually, and participants were reimbursed for their time. Using the Normalization Process Theory framework, we developed an interview guide to clarify the processes and conditions that influence whether and how an innovation becomes part of routine practice in complex health systems. RESULTS: Analysis of 29 interviews (n=17, 59% young people and n=12, 41% hospital staff) yielded 4 themes that were mapped onto 3 Normalization Process Theory constructs related to coherence building, cognitive participation, and collective action. Overall, digital innovations were seen as a beneficial complement to but not a substitute for in-person clinical services. The timing of delivery was important, with the agreement that digital therapeutics could be provided to patients while they were waiting to be assessed or shortly before discharge. Staff training to increase digital literacy was considered key to implementation, but there were mixed views on the level of staff assistance needed to support young people in engaging with digital innovations. Improving access to technological devices and internet connectivity, increasing staff motivation to facilitate the use of the digital therapeutic, and allowing patients autonomy over the use of the digital therapeutic were identified as other factors critical to integration. CONCLUSIONS: Integrating digital innovations into current models of patient care for young people presenting to hospital in acute suicide crises is challenging because of several existing resource, logistical, and technical barriers. Scoping the appropriateness of new innovations with relevant key stakeholders as early as possible in the development process should be prioritized as the best opportunity to preemptively identify and address barriers to implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".