Mobile Apps for Suicide Prevention: Review of Virtual Stores and Literature
Bibliographic record
Abstract
BACKGROUND: The best manner to prevent suicide is to recognize suicidal signs and signals, and know how to respond to them. OBJECTIVE: We aim to study the existing mobile apps for suicide prevention in the literature and the most commonly used virtual stores. METHODS: Two reviews were carried out. The first was done by searching the most commonly used commercial app stores, which are iTunes and Google Play. The second was a review of mobile health (mHealth) apps in published articles within the last 10 years in the following 7 scientific databases: Science Direct, Medline, PsycINFO, Embase, The Cochrane Library, IEEE Xplore, and Google Scholar. RESULTS: A total of 124 apps related to suicide were found in the cited virtual stores but only 20 apps were specifically designed for suicide prevention. All apps were free and most were designed for Android. Furthermore, 6 relevant papers were found in the indicated scientific databases; in these studies, some real experiences with physicians, caregivers, and families were described. The importance of these people in suicide prevention was indicated. CONCLUSIONS: The number of apps regarding suicide prevention is small, and there was little information available from literature searches, indicating that technology-based suicide prevention remains understudied. Many of the apps provided no interactive features. It is important to verify the accuracy of the results of different apps that are available on iOS and Android. The confidence generated by these apps can benefit end users, either by improving their health monitoring or simply to verify their body condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".