University Students’ Views on the Perceived Benefits and Drawbacks of Seeking Help for Mental Health Problems on the Internet: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: University students experience high levels of mental health problems yet very few seek professional help. Web-based mental health interventions may be useful for the university student population. However, there are few published qualitative studies that have examined the perceived benefits and drawbacks of seeking help for mental health problems on the Internet from the perspective of university students. OBJECTIVE: To investigate the attitudes of university students on mental health help-seeking on the Internet. METHODS: A total of 19 university students aged 19-24 years participated in 1 of 4 focus groups to examine their views toward help-seeking for mental health problems on the Internet. RESULTS: Perceived concerns about Web-based help-seeking included privacy and confidentiality, difficulty communicating on the Internet, and the quality of Web-based resources. Potential benefits included anonymity/avoidance of stigma, and accessibility. Participants reported mixed views regarding the ability of people with similar mental health issues to interact on the Internet. CONCLUSIONS: These factors should be considered in the development of Web-based mental health resources to increase acceptability and engagement from university 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".