Designing a graduate-level peer-wellness mentoring course
Bibliographic record
Abstract
Background: A growing body of research has indicated the importance of implementing community mental health initiatives in universities to promote student mental health and well-being. Designing and implementing community-based initiatives on campus can facilitate improved student well-being in a cost-effective way. Purpose: This paper provides a description of the implementation of a graduate-level university course grounded in a community mental health framework, which gave students the opportunity to become peer-mentors for other students within the university in exchange for course credit. Approach: The course took place over two semesters. In the first half of semester one, students engaged with theoretical and practical content that teaches them how to be a peer-mentor and support students’ needs. In the second half of the first semester, and over the course of the second semester, students used what they have learned to engage in experiential learning, where they acted as peer-mentors to their fellow university students. Conclusion: This university course provided mental health and well-being benefits to university students in a cost-effective manner while providing hands-on experience to students enrolled on the course to be peer-mentors. Implications: Experiential learning opportunities can be used to support student well-being while reducing the need for more specialist forms of mental health service provision.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".