Design of a Mindfulness Virtual Community: A focus-group analysis
Bibliographic record
Abstract
Mental illnesses are on the rise on campuses worldwide. There is a need for a scalable and economically sound innovation to address these mental health challenges. The aim of this study was to explore university students’ needs and concerns in relation to an online mental health virtual community. Eight focus groups ( N = 72, 55.6% female) were conducted with university students aged 18–47 (mean = 23.38, SD = 5.82) years. Participants were asked about their views in relation to online mental health platform. Three major themes and subthemes emerged: (1) perceived concerns: potential loss of personal encounter and relationships, fear of cyber bullying, engagement challenge, and privacy and distraction; (2) perceived advantages: anonymity and privacy, convenience and flexibility, filling a gap, and togetherness; and (3) desired features: user-centered design, practical trustworthy support, and online moderation. The analysis informed design features for a mindfulness virtual community.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".