Undergraduate Wellbeing and Mental Health Literacy at a Large Urban University: A Narrative Inquiry
Bibliographic record
Abstract
Student wellbeing and mental health have been a concern in higher education for the past decade. This subject has been studied through a variety of quantitative means including large scale surveys such as the National Campus Health Assessment and the Canadian Campus Wellbeing Study. These studies provide metrics to analyze what students’ struggles and supports, however, they don't provide an actionable strategy to improve wellbeing. This doctoral study uses narrative inquiry to investigate how undergraduate students define, learn about, and act to support their own wellbeing. The purpose of this study is to improve student wellbeing and reduce the frequency and duration of students’ suffering from mental health concerns through educational means. Through in-depth interviews and a focus group with nine upper year undergraduate students, this study identifies that 1) students arrive at university with no formal education in wellbeing and mental health literacy; 2) access to this education is not equally available through credit bearing courses at university; 3) the impact of co-curricular education is limited by its scope and depth due to the brief nature of one-off opt-in workshops; 4) learning about wellbeing and mental health often occurs when students are exhausted and overwhelmed due to a mental health concern or other challenges; 5) spiritual wellbeing is poorly understood by students; 6) work on campus provides some students with an opportunityto learn about wellbeing and mental health literacy and it supports their wellbeing. This study proposes a curriculum for a course in wellbeing and mental health literacy based on topics and teaching and learning methods that upper year undergraduates found valuable. Topics for this curriculum fall into the three domains: personal skills and knowledge, self-care strategies, and contextual knowledge. Highlights include autonomy, self-advocacy, terms and definitions, social influences on mental health as well as physical, occupational, and spiritual wellbeing. Based on the analysis of how students successfully learned about wellbeing, the proposed course would engage experiential learning and contemplative pedagogy, make topics personally relevant and solution focused, use a critical first-person learning approach, make learning action oriented, recognize different learning preferences and engage Universal Design for Learning. Keywords: higher education; contemplative pedagogy; wellbeing; mental health; narrative inquiry
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".