Redefining self-care through positive education: university student perspectives
Bibliographic record
Abstract
Faced with innumerable stressors and responsibilities, university students are particularly susceptible to mental health and well-being challenges. Self-care practices have been shown to help mitigate stress and health problems. One way university students can learn self-care practices is through positive education, the teaching of positive psychology. However, minimal research has assessed the role positive education plays in students’ self-care behaviours. Therefore, the purpose of this study was to explore the impact of an undergraduate positive education course on students’ self-care practices and, in turn, their mental health, quality of life, and happiness. Five undergraduate students participated in a focus group to discuss their experiences in a positive education course offered at a large Canadian university. With 75 students across two sections, the course consisted of 12 weekly three-hour sessions, combining theoretical teachings in positive psychology with practical activities to foster student well-being and flourishing. Reflexive thematic analyses revealed that the course encouraged students to engage in self-care practices which, by extension, enhanced their mental health, quality of life, and happiness. There was a general consensus among participants that these improvements stemmed from increased self-kindness, slowness, awareness, and particularly social connection. In fact, the participants learned to prioritize both themselves and their social relationships as essential aspects of self-care. These findings support the importance of offering positive education courses in university settings and demonstrate the benefits of promoting self-care as both an individual and collective practice in these 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".