The Positive Writing on Mood States: Empirical Study
Bibliographic record
Abstract
Introduction Positive writing (PW) consists in a written treatment of real or imagined events, processed with a positive connotation. The technique has been proven useful and effective for increasing psychological well-being. It derives from the expressive writing (EW) methodology developed by James Pennebaker. Objectives The objective of this study is to analyze the effects of positive writing (PW) in a group of healthy subjects. The psychological variables measured following the application of PW are six mood states: tension, depression, anger, vigor, fatigue and confusion. These are preliminary data from work that is still in progress. Methods Two groups were randomly formed (one experimental and one control) and wrote for 3 consecutive days on different topics. The experimental group wrote for 20 minutes a day about the most rewarding experience of their life, while the control group described, again for 20 minutes a day, a topic with a low emotional connotation (description of their home). Three administrations (baseline, 3-day follow-up and 10-day follow-up) of the POMS (Profile of Mood States) psychological test were carried out on study participants. A statistical analysis such as analysis of variance (2-way ANOVA for repeated measures) was used to analyze the effects of positive writing in relation to the different parameters considered, between the groups (Experimental Group vs Control Group) in three different times (baseline, 3 days, 10 days). Results Statistically significant decreases were recorded in the experimental group in confusion (Factor C) in the 10-day measurement (7.44 VS 5.00 p < 0.01) and in fatigue (factor S) (5, 94 VS 3.88; p < 0.05). Conclusions These data demonstrate how positive writing can lead to beneficial psychological effects. In particular, this study examined the effects of writing about one’s real life experiences and highlighted beneficial psychological/cognitive effects (decreased confusion) and psychophysical (decreased feelings of fatigue). Focusing attention on one’s positive experiences therefore produces improvements on a cognitive level for the sensations that concern clarity and linearity of thought and reduction of feelings of psycho-physical fatigue. Disclosure of Interest None Declared
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".