Supplemental Material for Mobile Applications for Individuals Affected by a Traumatic Event: A Systematic Review of Qualitative Findings
Bibliographic record
Abstract
Access to effective interventions aimed at reducing the mental health consequences of exposure to traumatic events is hampered by barriers to treatment. Mobile applications (apps) are one way to overcome these barriers. However, the effectiveness of apps in reducing posttraumatic stress symptoms (PTSS) is limited, which could be explained by low user engagement. A better understanding of the needs and preferences of individuals with PTSS could help in developing apps that are more engaging and possibly more effective. This review aims to synthesize qualitative findings from studies examining the subjective experiences of individuals who use apps for PTSS. A systematic search was conducted in Pubmed and APA PsycInfo. Empirical studies that report qualitative data and focus on one or more apps designed for the self-assessment or the self-management of PTSS were included. Sixteen articles focusing on 14 apps met the inclusion criteria. Participants reported barriers (e.g., lack of perceived benefits), facilitators (e.g., ease of use), benefits (e.g., improved mental health), and adverse effects (e.g., increased symptoms) related to the use of the apps. They also made suggestions aimed at improving user experience, such as increasing customization. In conclusion, developing apps with a user-centered approach, promoting social support through the use of the apps, and including gamification elements might increase user engagement with apps for PTSS. Further research should test if that is the case.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.000 |
| 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 teacher head, 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".