The Role of Trait and State Mindfulness in Cognitive Performance of Male Adolescents
Bibliographic record
Abstract
Purpose: The number of mindfulness intervention projects is continually increasing. Within the educational environment, mindfulness has purported links to well-being, positive behaviour, educational and cognitive performance. Trait mindfulness is related to rational thinking and better performance in cognitive tests, suggesting that innate mindfulness ability contributes to self-regulation ability and thus the efficacy of mindfulness interventions. The current study investigates whether mindfulness is a moderating factor. It examines correlations between cognitive performance and trait mindfulness. The study investigates the influence of trait mindfulness on the ability of students to enter state mindfulness in an attempt to understand the role both types of mindfulness may have on cognitive performance. Participants and Method: Two-hundred and five male students aged fifteen and sixteen completed the adolescent version of the Mindfulness Awareness Scale, the Cognitive Reflection Test, and the Toronto Mindfulness Scale. Results: Hierarchical regression analysis found that state mindfulness was a predictor of cognitive reflection ability. ANOVA also found that having either trait or state mindfulness predicted higher cognitive reflection scores, but only state mindfulness had a significant effect on cognitive reflection. Trait mindfulness was not a moderating factor. Conclusion: Both state and trait aspects of mindfulness ability influence cognitive performance. Those with higher trait mindfulness ability are better able to enter state mindfulness and thus had better cognitive reflection scores. However, where it is possible to induce state mindfulness into those with low trait mindfulness, CRT scores were also higher although not significantly so.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".