Influence of a Brief Online Mindfulness Intervention on Metacognition, Cognition, and Emotional Outcomes Among University Students: A Randomized Longitudinal Trial
Bibliographic record
Abstract
Mindfulness-based interventions (MBIs) have been previously linked with improved cognition and reduced stress, anxiety, and depression. Yet, traditional MBIs are lengthy and resource intensive. Brief and online MBIs represent a bridge into more extensive practice—but questions of whether these protocols improve cognition and affect remain. The present experiment used a randomized longitudinal design to assess the effects of a 31-day, 15 min daily mindfulness program compared to a podcast control on a battery of cognitive and self-report measures in a sample of university students. Results indicated that, over the course of the study, the MBI group found their intervention less challenging, more enjoyable, more relaxing, more engaging, and more useful compared to the podcast control group. MBI participants also increased in state and dispositional mindfulness and state metacognition following the intervention relative to the Podcast group. However, both groups scored comparably on all cognitive and affective post-intervention measures, with equivalence testing suggesting that the observed effect sizes in the present study were significantly smaller than in some previously reported effects. Taken together, the results suggest that while brief online MBIs can elicit near transfer to proximal domains (e.g., mindfulness, metacognition), more work is needed to make strong claims that these MBIs elicit far transfer to cognitive and affective domains.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".