Understanding Self-Regulation Techniques and Their Impact on Undergraduate Students' Stress and Wellbeing
Bibliographic record
Abstract
University is a significant transitional period marked by numerous psychological, social, and academic challenges. Students must adjust to new environments, manage increased academic workloads, and navigate shifting social dynamics, all of which contribute to heightened stress levels (Bewick et al., 2010). Research indicates that psychological distress tends to increase over the course of undergraduate studies, with students experiencing a decline in psychological well-being that persists throughout their degree. In Ontario, many students report struggling with their mental health, often citing limited coping strategies and barriers to seeking support (Moghimi et al., 2023). Among these barriers are stigma surrounding mental health concerns, a lack of awareness about available resources, and perceptions that universities do not provide adequate mental health support. Consequently, many post-secondary students believe that universities must implement more accessible and diverse mental health resources to meet their needs effectively. Given these challenges, there is an urgent need for Canadian universities to develop and implement evidence-based, accessible mental health interventions that can support students throughout their undergraduate journey. One promising approach involves teaching students self-regulation techniques that promote resilience and psychological well-being. Self-regulation refers to an individual's ability to manage emotions, thoughts, and behaviours in response to stressors, and it has been widely recognized as a key factor in maintaining mental health during periods of transition and change. Thus, the current study aims to investigate the impact of online wellness skills intervention training (e.g., mindfulness, paced/relaxed breathing, and journaling) on undergraduate students' stress and well-being.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".