Perceptions and Attitudes Toward a Mobile Phone App for Mental Health for College Students: Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Many college students who have mental health issues do not receive professional care for various reasons. Students who do not receive help often have both short- and long-term adverse health outcomes. Mobile apps for mental health services such as MySSP, a service provided to college students through their university, may help eliminate barriers to seeking mental health care and result in more positive outcomes for college students. OBJECTIVE: This qualitative study aims to better understand college students' perceptions and attitudes toward the adoption and use of a mobile phone app for mental health, MySSP, using the technology acceptance model (TAM). METHODS: A series of nine focus groups were conducted with college students (N=30) between February and May 2019 at a large, public Midwestern university. The moderator's guide was based on the TAM, and focus group sessions primarily focused on the use and knowledge of apps for mental health, specifically, MySSP. The focus group transcriptions were hand-coded to develop a set of themes that encompassed students' perceptions and attitudes toward MySSP. RESULTS: The analysis of the focus groups suggested the following themes: (1) existing awareness of the app, (2) perceived usefulness, (3) perceived ease of use, (4) attitudes toward apps for mental health and MySSP, and (5) social influence. CONCLUSIONS: The results of this study provide deeper insights into the perceptions of a mobile app for mental health among college students. Future research should explore the specific contexts in which an app for mental health will be most effective for 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".