Innovative Approaches to Student Wellbeing: The Role of Advanced Technology in Mental Health Practices
Bibliographic record
Abstract
The mental health crisis stands as one of the greatest challenges we face today. One group particularly at risk are post-secondary students. Despite this, many students struggle to find adequate support for their mental health due to availability or accessibility. Considering the scarcity of access to therapeutic resources, this dissertation proposes the use of brief online mental health interventions as a feasible alternative for supporting student wellbeing. Across four studies, it examines the potential of online interventions for enhancing the wellbeing of post-secondary students, with a specific focus on the Mindfulness-to-Meaning Theory (MMT) and the integration of advanced technologies. The initial two studies validated the transition of mindfulness interventions to online formats and their impact on students’ emotional regulation and stress management. Subsequent studies focused on the inclusion of user-centric materials, such as customized training modules and Large Language Models (LLMs)-powered chatbots, to provide individualized intervention recommendations and improve overall participants’ engagement. Key findings highlight the potential of online interventions to overcome barriers associated with traditional mental health services, such as accessibility, stigma, and engagement challenges. Brief online mindfulness interventions may support students’ wellbeing by fostering positive emotional states and emotion regulation skills. The results also provide support for the MMT pathway, suggesting the sequential development of decentering and positive reappraisal skills as mechanisms underlying enhanced wellbeing. The integration of advanced technologies demonstrated efficacy compared to traditional online training platforms, with improved overall wellbeing outcomes. In conclusion, this dissertation highlights the potential of brief online interventions to improve mental health support for post-secondary populations. Providing these mass-administrable, personalized interventions will allow students to better face academic stressors through improving coping skills and overall happiness.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".