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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".