Privacy Issues in the Development of a Virtual Mental Health Clinic for University Students: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: There is a growing need to develop online services for university students with the capacity to complement existing services and efficiently address student mental health problems. Previous research examining the development and acceptability of online interventions has revealed that issues such as privacy critically impact user willingness to engage with these services. OBJECTIVE: To explore university student perspectives on privacy issues related to using an online mental health service within the context of the development of an online, university-based virtual mental health clinic. METHODS: There were two stages of data collection. The first stage consisted of four 1.5-hour focus groups conducted with university students (n=19; 10 female, 9 male, mean age = 21.6 years) to determine their ideas about the virtual clinic including privacy issues. The second stage comprised three 1-hour prototype testing sessions conducted with university students (n=6; 3 male, 3 female, mean age = 21.2 years) using participatory design methods to develop and refine a service model for the virtual clinic and determine student views on privacy within this context. RESULTS: The students raised a number of issues related to privacy in relation to the development of the university virtual clinic. Major topics included the types of personal information they would be willing to provide (minimal information and optional mental health data), concern about potential access to their personal data by the university, the perceived stigma associated with registering for the service, and privacy and anonymity concerns related to online forums contained within the virtual clinic. CONCLUSIONS: Students would be more comfortable providing personal information and engaging with the virtual clinic if they trust the privacy and security of the service. Implications of this study include building the clinic in a flexible way to accommodate user preferences.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".