Impact of Digital Safety Plan Activation on Subsequent Emergency Departments Visits Following an Initial Suicide Attempt: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Suicide is a significant global public health concern. Individuals with suicidal behaviors often seek help in emergency departments (ED), making mental health providers critical to suicide prevention. Brief interventions such as safety planning are essential in these settings. However, there is a limited understanding of how mobile digital safety planning apps can aid in secondary suicide prevention. Objective: This study evaluated the effectiveness of a digital safety plan, delivered through the MeMind app, in reducing ED visits associated with suicidal behavior (ie, suicidal ideation or attempt). Methods: A one-year follow-up was conducted for individuals who presented to the ED for an index event of suicidal behavior (N=78). Participants were provided with a digital safety plan on their mobile devices and instructed to activate it during future suicidal crises. Results: At follow-up, participants who activated the digital safety plan showed a 50% lower likelihood of returning to the ED, when compared to those who did not activate it. Conclusions: These findings suggest that digital safety planning may serve as a scalable and accessible intervention with the potential to significantly contribute to suicide prevention efforts.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".