Understanding the influence of quality of living course on undergraduate mental health: A qualitative study
Bibliographic record
Abstract
University students have lower levels of mental health compared to the general population. Since the beginning of the COVID-19 pandemic, a further decline in university student mental health has been reported, emphasizing the growing concern for this population. Positive psychology courses could provide undergraduate students with the tools and techniques to cope with academic demands, life stresses, and a global pandemic. One such course, the Quality of Living course, exists at the University of Ottawa; however, its impact on student mental health has yet to be examined. Thus, the purpose of this study was to explore the perspectives of the students in this course and how they believe the course has influenced their mental health 1-year after taking the course and during the ongoing COVID-19 pandemic. Semi-structured interviews were conducted with students (n=10) who took the course in the Winter 2020 semester. Thematic analysis of the transcripts is currently underway to develop common themes from the data. High-level overview of the data suggests that the course provides techniques that are transferable to real-life situations, and these techniques have been maintained 1-year after completion of the course. Additionally, students reported that the course has improved their overall mental health throughout the COVID-19 pandemic. These results suggest that positive psychology courses like the one investigated in this study could play a valuable role in improving undergraduate student mental health. This knowledge fills many gaps in the literature and can be used to inform the development and implementation of future positive psychology courses.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".