Preliminary Support for a Technology-Enhanced Suicide Prevention Intervention for College Students: A Mixed-Method Pre-Implementation Study (Preprint)
Bibliographic record
Abstract
Background: Self-injurious thoughts and behaviors, including suicidal ideation, planning, attempts, and nonsuicidal self-injury, are a significant public health concern among college students. Community college students face elevated mental health risks and persistent barriers to traditional services, particularly those from underserved and ethnoracially minoritized backgrounds, underscoring the need for scalable, accessible digital mental health interventions. Objective: This study conducted a mixed methods evaluation of the initial reactions of college students to a novel technology-enhanced suicide prevention intervention (TE-SPI), with particular focus on feedback regarding a mobile app prototype (BRITE) designed to support coping skill use and mood monitoring, as well as proposed nondemanding caring contact text messages. Methods: A mixed methods approach using the technology acceptance model (TAM) was used to assess the TE-SPI among 20 ethnoracially diverse college students (mean age 22.8, SD 3.9) from a community college (n=10) and a 4-year university (n=10). Participants were shown the BRITE app prototype on a study iPad and asked to provide feedback on its perceived ease of use, usefulness, and relevance to student needs. Quantitative measures assessed initial impressions of satisfaction and usability, while qualitative analyses examined participants' reactions to the app prototype and preferences regarding potential caring contact text messages. Results: Participants generally reported favorable initial impressions of the BRITE app prototype, with 90% (18/20) describing it as helpful and 85% (17/20) user-friendly; 90% (18/20) also indicated that they would recommend it to peers in distress. Qualitative feedback supported these findings, highlighting the app's perceived accessibility, relevance to student life, and potential usefulness during moments of distress. Participants also offered concrete suggestions for app refinements (eg, mood scale customization, gamification, and language options) and for useful caring contact messages they would want to receive. In total, 55% (11/20) of participants said that they would want to receive text messages as part of TE-SPI, while 25% (5/20) reported maybe or possibly wanting to receive messages. Conclusions: Findings offer preliminary insight into how college students perceive the BRITE app prototype and proposed caring contact message, highlighting concrete feedback for app refinement prior to real-world testing. This study provides early user feedback that can inform further development of the TE-SPI model. Future research should examine actual use, acceptability, feasibility, and clinical impact among diverse college students.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".