A DEVELOPMENTAL–CONTEXTUAL MODEL OF COUPLE SYNCHRONY: CENTRAL TENETS AND EMPIRICAL APPLICATION
Bibliographic record
Abstract
Abstract Most current models of social dynamics and health focus on individuals; our developmental-contextual model of couple synchrony (CoSynch) proposes a uniquely dyadic approach and deliberately moves to a N + 1 perspective. CoSynch emphasizes the importance of between-person synchrony for shaping development and health, which refers to the way in which physiological states and health behaviors fluctuate in sync between romantic partners. Synchrony is proposed to follow a u-shaped curve across adulthood, with younger and older couples showing greater synchrony than middle-aged couples, and with greater diversification of synchrony in very old age. We assume that synchrony is shaped by the closeness and shared contexts of couples, and is correlated with individual and dyad characteristics (e.g., attachment). Prior research has linked synchrony to perspective taking and effective collaboration. Thus, synchrony is assumed to be important for supportive interactions in couples managing chronic disease. As an empirical illustration, we apply the CoSynch model to analyze 992 daily life assessments from 11 Swiss couples in which one partner had Type II diabetes. Specifically, we used a smartphone and smartwatch system (DyMand) that captured daily social contexts and heart rate synchrony. Couples showed small to moderate interconnections in their heart rate fluctuations, which tended to be stronger when engaged in conversation with the partner. We will discuss the utility of using wearables to better understand health-relevant social developmental dynamics. Insights from collecting such data could be used to inform future interventions using technology to promote healthy aging and adaptive disease management.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".