A dynamic state-space analysis of interpersonal emotion regulation in couples who smoke
Bibliographic record
Abstract
Regulating emotions in interpersonal contexts requires managing one’s own emotion, a partner’s emotion, and the emotional tone of the relationship (e.g., conflict and intimacy). This multifaceted regulatory challenge, often referred to as “relationship-focused coping,” has been associated with health outcomes, but the real-time emotional processes involved are understudied. We use state-space grids (a recently developed graphical method) to investigate dynamic sequences of emotional experience (positive vs. negative) and relationship-focused coping intentions (to protect vs. engage one’s partner) taken from 26 couples in which one or both partners were smokers, while they discussed a health-related disagreement during a nonsmoking baseline and then while smoking. State-space indicators of contingent emotion-coping sequences showed evidence of both successful regulation (associated with improving emotional state) and unsuccessful regulation (associated with worsening emotional state). The pattern of results suggests that interpersonal emotion regulation may interfere with smoking cessation differently depending upon whether one or both partners smoke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".