A Community Mental Health and Well-Being University Level Course: Design and Implementation
Bibliographic record
Abstract
Background: Community mental health is growing discipline in psychology that recognizes the importance of creating a community that fosters wellness. Although universities provide many individuals a sense of community, little research has examined how community mental health interventions can be implemented into a classroom setting. Purpose: This paper provides a proof of concept of a university course that was created to give students the opportunity to interact with their campus community while receiving course credit over two semesters. Approach: In the first semester, the course provided students with content and theory as it relates to community mental health, well-being, and health promotion. The second semester implemented experiential learning, where students applied knowledge and skills to a placement related to mental health and well-being within their university. Conclusions: This university course can provide benefits to the university (e.g., cost-efficiency), the students (e.g., networking), and the community (e.g., accessible mental health services). This research presents a course framework that other post-secondary institutions can build upon and implement into their own programs. Implications: Future research should focus on implementing experiential learning courses that provide opportunities in the mental health field for undergraduate psychology students to facilitate post-graduate student success.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".