Web-based training for post-secondary student well-being during the pandemic: a randomized trial
Bibliographic record
Abstract
Background: The COVID-19 pandemic has been a highly stressful period where post-secondary education moved to online formats. Coping skills like decentering and reappraisal appear to promote stress resilience, but limited research exists on cultivating these skills in online learning contexts.Methods: In a three-arm randomized trial design, we evaluated three-week, web-based interventions to gauge how to best cultivate mindfulness and stress-reappraisal skills and whether the proposed interventions led to improved mental health. Undergraduate participants (N = 183) were randomly assigned to stress mindset, mindfulness meditation, or mindfulness with choice conditions.Results: At the study level (baseline vs. post-intervention), decentering improved across all conditions. Mindfulness with choice significantly decreased negative affect and rumination compared to stress mindset, while stress mindset significantly enhanced stress mindset skills compared to both mindfulness groups. At the daily level (three sessions per week), stress mindset significantly increased positive affect compared to mindfulness meditation.Conclusions: Results suggest that student mental health can be remotely supported through brief web-based interventions. Mindfulness practices seem to be effective in improving students' negative mood and coping strategies, while stress mindset training can help students to adopt a stress-is-enhancing mindset. Additional work on refining and better matching students to appropriate interventions is needed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".