A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study
Bibliographic record
Abstract
BACKGROUND: Long wait times for mental health treatments may cause delays in early detection and management of suicidal ideation and behaviors, which are crucial for effective mental health care and suicide prevention. The use of digital technology is a potential solution for prompt identification of youth with high suicidality. OBJECTIVE: The primary aim of this study was to evaluate the use of a digital suicidality notification system designed to detect and respond to suicidal needs in youth mental health services. Second, the study aimed to characterize young people at different levels of suicidal ideation and behaviors. METHODS: Young people aged between 16 and 25 years completed multidimensional assessments using a digital platform, collecting demographic, clinical, social, functional, and suicidality information. When the suicidality score exceeded a predetermined threshold, established based on clinical expertise and service policies, a rule-based algorithm configured within the platform immediately generated an alert for treating clinicians. Subsequent clinical actions and response times were analyzed. RESULTS: A total of 2021 individuals participated, of whom 266 (11%) triggered one or more high suicidal ideation and behaviors notification. Of the 292 notifications generated, 76% (222/292) were resolved, with a median response time of 1.9 (range 0-50.8) days. Clinical actions initiated to address suicidality included creating safety plans (60%, 134/222), conducting safety checks (18%, 39/222), psychological therapy (8%, 17/222), transfer to another service (3%, 8/222), and scheduling of new appointments (2%, 4/222). Young people with high levels of suicidality were more likely to present with more severe and comorbid symptoms, including low engagement in work or education, heterogenous psychopathology, substance misuse, and recurrent illness. CONCLUSIONS: The digital suicidality notification system facilitated prompt clinical actions by alerting clinicians to high levels of suicidal ideation and behaviors detected among youth. Further, the multidimensional assessment revealed complex and comorbid symptoms exhibited in youth with high suicidality. By expediting and personalizing care for those displaying elevated suicidality, the digital notification system can play a pivotal role in preventing rapid symptom progression and its detrimental impacts on young people's mental health.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".