Insights from User Reviews to Improve Suicide Prevention Apps: A Machine Learning and Thematic Analysis-Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we conducted a comprehensive evaluation of user reviews of suicide prevention apps using machine learning (ML) and thematic analysis to assess usability and user experience. Given the critical public health issue of suicide, mobile applications have emerged as potential intervention tools. However, user perspectives are underexplored. Our analysis encompassed user reviews from 39 suicide prevention apps, totaling 110,338 reviews. We employed natural language processing for sentiment analysis and implemented five ML classifiers with Random Forest achieving the highest F1 score (87.19%). The thematic analysis revealed seven negative themes including access to therapy and privacy concerns, alongside five positive themes such as app functionality and user engagement. This research quantitatively and qualitatively evaluates user sentiments, providing insights that can guide developers and policymakers in enhancing the design of suicide prevention apps, thereby addressing mental health challenges in the digital landscape.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it