The road to greater well-being: exploring the impact of an undergraduate positive education course on university students’ well-being
Bibliographic record
Abstract
Abstract The profile of subjective well-being (SWB) in university students is perturbing in many respects. Indeed, university students are in need of tools to combat stress and promote SWB now more than ever given the adverse repercussions of the COVID-19 pandemic. Positive education could serve as a SWB tool to help university students deal with academic, personal, and global stressors. While a number of studies have quantitatively reported the impact of positive education on student SWB, few have considered students’ experiences and perceptions of changes in their SWB as a result of taking a positive education course. Therefore, the purpose of this study was to qualitatively explore university students’ experiences in a positive education course and their perceptions of its influence on their SWB immediately after taking the course. Undergraduate students (n = 17) who had taken a positive education course during the Winter term of 2020 (January–April) were recruited via volunteer sampling. Data were collected by means of semi-structured interviews and analyzed using reflexive thematic analysis. Analyses revealed that the course improved the students’ SWB, self-compassion, mindfulness, and optimism. Mechanisms such as greater self-reflection, implementation of intentional positive activities, and big picture thinking underlie these reported improvements. Our findings support positive education’s effectiveness in enhancing student SWB and expand on the current literature by proposing novel mechanisms linking positive education to enhanced student SWB, self-compassion, mindfulness, and optimism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".