Arousal transmission and attenuation in mother–daughter dyads during adolescence
Bibliographic record
Abstract
Abstract Load sharing is the process through which the emotional burdens associated with challenging situations are distributed among members within close relationships. One indicator of load sharing is efficient emotion regulation, and load sharing is related to high physical and relationship closeness between partners. The purpose of the current study was to examine load sharing in mother–daughter dyads across different emotion contexts during adolescence. We examined load sharing via mothers' and daughters' electrodermal activity, which indicates sympathetic nervous system arousal. Sixty‐six adolescent girls (Mage = 15.15 years) and their mothers participated in two 4‐min discussions, about negative and positive emotional experiences. Mothers and daughters self‐reported relationship closeness, and physical closeness was experimentally manipulated by randomly assigning mothers to either touch or not touch their daughter's hand during the discussions. Evidence of load sharing was observed for both mothers and daughters in terms of lower arousal levels and greater arousal transmission (i.e., picking up on partners' arousal) at greater physical and relationship closeness, although the specific pattern of results differed for mothers and daughters and across emotion contexts. Taken together, the results of this study demonstrate that load sharing is an important interpersonal dynamic in mother–daughter dyads during adolescence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".