Characteristics of Suicide Prevention Apps: A Content Analysis of Apps Available in Canada and the United Kingdom
Bibliographic record
Abstract
Abstract Objective We aimed to examine the characteristics, features, and content of suicide prevention mobile apps available in app stores in Canada and the United Kingdom. Design Suicide prevention apps were identified from Apple and Android app stores between March-April 2023. Apps were screened against predefined inclusion criteria, and duplicate apps were removed. Data were then extracted based on descriptive (e.g., genre, app developer), security (e.g., password protection), and design features (e.g., personalization options). Content of apps were assessed using the Essential Features Framework. Extracted data were analyzed using a content analysis approach including narrative frequencies and descriptive statistics. Results Fifty-two (n=52) suicide prevention apps were included within the review. Most were tailored for the general population and were in English language only. One app had the option to increase app accessibility by offering content presented using sign language. Many apps allowed some form of personalization by adding text content, however most did not facilitate further customization such as the ability to upload photo and audio content. All identified apps included content from at least one of the domains of the Essential Features Framework. The most commonly included domains were sources of suicide prevention support, and information about suicide. The domain least frequently included was screening tools followed by wellness content. No identified apps had the ability to be linked to patient medical records. Conclusions The findings of this research present implications for the development of future suicide prevention apps. Development of a co-produced suicide prevention app which is accessible, allows for personalization, and can be integrated into clinical care may present an opportunity to enhance suicide prevention support for individuals experiencing suicidal thoughts and behaviours. Article Summary Strengths and limitations of this study This app review used an established method for systematically identifying and examining suicide prevention apps, which has been successfully used previously. There is potential for overlap between different domains of the Essential Features Framework, which could lead to changes in reporting of percentages relating to app review findings. Only apps available in the UK and Canada in the English language were assessed. Current provision and content of suicide prevention apps may differ across countries, including those available in lower- and middle-income countries. Due to resource and time constraints, the quality of apps were not assessed.
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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 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".