Use of Online Tools for Mental Health Among Racially and Ethnically Diverse College Students: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression symptoms have been rising among college students, with many increasingly meeting the criteria for 1 or more mental health problems. Due to a rise in internet access and lockdown restrictions associated with the COVID-19 pandemic, online mediums, such as teletherapy, repositories for mental health information, discussion forums, self-help programs, and online screening tools, have become more popular and used by college students to support their mental health. However, there is limited information about individual-level factors that lead college students to use these online tools to support their mental health. OBJECTIVE: This mixed methods study aimed to examine the associations between demographics, symptom severity, mental health literacy, stigma, attitudes, and self-efficacy and the use of online tools to seek psychological information and services among racially and ethnically diverse college students. This study also aimed to qualitatively characterize types of online tools used, reasons for using tools or lack thereof, and perceived helpfulness of tools. METHODS: Undergraduate students (N=123) completed validated measures and provided open-ended descriptions of the types of online tools they used to seek psychological information and services and their reasons for using those tools. Logistic regression analyses were used to test associations of online tool use to seek mental health information and hypothesized predictors. Descriptive statistics were conducted to examine online tool types, reasons for using online tools, and helpfulness explanations. RESULTS: In total, 49.6% (61/123) of the participants used online tools (eg, search engines) to seek mental health information, while 30.1% (37/123) used online tools (eg, medical websites) to seek mental health services. Mental health literacy (P=.002; odds ratio 1.14, 95% CI 1.05-1.24) was associated with greater use of online tools to seek mental health information. None of the hypothesized variables predicted online tool use to seek mental health services. In total, 82% (50/61) of participants who sought information found online tools somewhat helpful, while 49% (18/37) of participants who sought services found online tools very helpful. Of the students who did not use online tools to seek information, 19% (12/62) reported it was because they did not know which online tools to use and 31% (19/62) stated they would be encouraged to use online tools if it was recommended by professionals, therapists, family, or friends. Of the students who did not use online tools to seek services, 33% (28/86) reported it was because they did not think mental health help was necessary. CONCLUSIONS: These findings highlight the use of online tools to provide mental health information and connect to professional services, suggesting that online tools are widely used to access mental health support.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".