Bibliographic record
Abstract
Written emotional expression has garnered significant evidence as a therapeutic tool for the processing of traumatic life events (Frattaroli, 2006; Pennebaker, 1997). However, its underlying mechanisms are still not fully understood or clearly defined. In this study, we predicted that written emotional expression exercises could serve as a mindfulness process. The goals of this study were (a) to test whether the writing process enhances mindfulness levels and (b) whether we can enhance mindfulness levels by building on a traditional writing instruction. To pilot this exercise, we modified the instructions of the traditional writing exercise to instruct individuals how to exercise mindfulness in their writing. Participants (N = 40) were randofmly assigned to either the traditional-writing group (TG) based on the Pennebaker instructions (Pennebaker, Kiecolt-Glaser, & Glaser, 1988) or to the mindfulness-enhanced group (MG), which incorporated mindfulness-based instructions (Levitt et al., 2004; Hayes & Smith, 2005) for writing about students' most stressful life experience. The Toronto Mindfulness Scale (TMS) was used to measure reports of curiosity and decentering before and after the writing exercise. Results revealed that decentering increased after participants engaged in the traditional writing exercise but not the mindfulness-enhanced exercise. Contrary to our prediction, curiosity reports did not change significantly overtime, and the mindfulness-enhanced writing did not differentially enhance individuals' mindfulness levels compared to the traditional writing exercise. These findings provide preliminary evidence that decentering may serve as an underlying mechanism in expressive writing. Future studies should replicate these findings and assess mindfulness changes in expressive writing over the course of several days.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".