University Student Mentor Experiences of the Comfort Corner Well-being Program
Bibliographic record
Abstract
University Students’ psychological well-being can impact their health, academic performance, retention, and ability to complete university. Participation in peer mentoring well-being programs has been found to help improve student outcomes. This study aimed to explore student mentors’ experiences of a co-designed university student peer-to-peer well-being program, the “Comfort Corner”. The study utilised a sequential mixed methods design collecting survey and interview data from student mentors about their experiences, knowledge and attitudes about psychological well-being as well as their skills and confidence to support the psychological well-being of their peers. Thirteen student mentors completed pre-post program surveys which revealed higher post-program scores on assessments related to their perceived communication skills (pre-test M=84.3, SD=13.7, post-test M=86.7, SD=11.5) and their knowledge about psychological well-being (pre-test M=10.9, SD=5.4, post-test M=15.6, SD=2.7). All 8 student mentors who completed a post-program satisfaction survey indicated that the peer-mentoring program improved their skills and was very useful (100% respectively). Thematic analysis of interviews conducted with 10 student mentors revealed 2 themes, 1) understanding psychological well-being and, 2) knowing how to engage and help others as accounting for improvements in student mentors’ skills and knowledge. Student mentors described their experience with Comfort Corner under a central theme, ‘fostering a community of support for students on campus’, they felt Comfort Corner provided welcoming, safe, and supportive space for students. These findings revealed the benefits of co-design using a student as partners framework for a peer mentoring well-being program in improving areas of student mentors’ skills and knowledge as well as promoting a sense of belonging and connection for students enrolled in higher education.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".