Does Feeling Bad, Lead to Feeling Good? Arousal Patterns during Expressive Writing
Bibliographic record
Abstract
Different psychotherapy theories describe process patterns of emotional arousal in contradictory ways. To control both treatment and therapist responsivity, this study sought to test dynamic patterns in the arousal of negative affect using a controlled experimental study of expressive writing. There were 261 participants (78% women; M = 21 years old; 56% White) who suffered unresolved traumas who were randomly assigned to an expressive writing task and asked to write about their deepest thoughts and feelings, or to a writing control. Participants wrote for 15 min on three consecutive days, completing the Positive Affect and Negative Affect Scale before and after each visit. Data across 6 time points were subjected to hierarchical linear modeling (HLM) and pattern analyses. Session-by-session (24 hr periods), the expressive writing group showed an overall linear decrease in negative affect (β = −2.273, p < .001). However, in pre- to post-session ratings (15 min periods), the expressive writing group also demonstrated increases in negative affect (β = 6.467, p < .001). Neither of these patterns were observed in the control group. Pattern analysis demonstrated 69.8% of cases in the expressive writing group perfectly or almost perfectly followed a predicted zig-zag pattern ( p < .01). No control cases showed this pattern. Findings demonstrate how the habituation/inhibition hypothesis (“it gets easier as one gets over it”) and the meaning-making hypothesis (“it gets worse before it gets better”) can both be supported, each at different scopes of analysis. Implications clarify the role of emotional arousal in change.
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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 | 0.005 |
| 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 | 0.001 | 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".