Changing Emotion with Emotion: The Best Sequence Depends on the Target Concern
Bibliographic record
Abstract
Abstract Background Lingering anger and sadness about a interpersonal interaction is a common problem. However, resolving those feelings may depend on the sequence in which feelings are experienced. Method Using 167 participants, two experimental groups were identified based on presenting emotional concern: individuals with predominantly lingering anger about an interpersonal interaction (i.e., angry group, n = 70), and individuals with predominantly lingering sadness about an interpersonal interaction (i.e., sad group, n = 97). Participants completed written interventions to facilitate anger and sadness in one of two randomly assigned conditions (i.e., anger-before-sadness condition or sadness-before-anger condition), which differed only by the order in which participants were guided to feel anger and sadness. Results In the angry group, those guided to feel anger-before-sadness reported a greater decline in the intensity of their presenting anger than those guided to feel sadness-before-anger (d = − 0.56). In contrast, in the sad group, those guided to experience sadness-before-anger reported a greater decrease in lingering sadness than those guided to experience anger-before sadness (d = − 0.26). Conclusions Strategically ordered sequence of emotion states seems to have a synergistic impact in facilitating change, which has implications for how therapists might best choose to guide client process in psychotherapy.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".