Exploring daily affective changes in university students with a mindful positive reappraisal intervention: A daily diary randomized controlled trial
Bibliographic record
Abstract
Brief and cost-effective interventions focused on emotion regulation techniques can buffer against stress and foster positive functioning. Mindfulness and positive reappraisal are two techniques that can mutually enhance one another to promote well-being. However, research testing the effectiveness of interventions combining mindfulness and reappraisal is lacking. The current pilot examined the effect of a combined mindful-reappraisal intervention on daily affect in a 5-day diary study with 106 university students. Participants were randomized to a mindful-reappraisal intervention (n = 36), a reappraisal-only intervention (n = 34), or an active control activity (n = 36). All participants described a negative event each day but only reappraised the event in the intervention conditions. Using multilevel growth modelling, results indicated that negative affect in both interventions declined over 5 days compared to the control; however, there were no differences in the growth of positive affect. Compared to reappraisal-only, the mindful-reappraisal group reported overall lower daily negative affect and marginally higher daily positive affect over the 5-day intervention. These findings suggest that brief daily practice combining mindfulness and positive reappraisal can be trained as a self-regulatory resource to promote positive affect and buffer negative affect above and beyond reappraisal practice alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".