Affect regulation and allostatic load over time
Bibliographic record
Abstract
Emerging work suggests that affect regulation strategies (e.g., active coping, anger expression) predict disease and mortality risk, with sometimes divergent estimates by sex or education levels. However, few studies have examined potential underlying biological mechanisms. This study assessed the longitudinal association of affect regulation with future allostatic load. In 2004–2006, 574 participants from the Midlife in the United States study completed validated scales assessing use of nine general and emotion-specific regulatory strategies (e.g., denial, anger expression). As a proxy for how flexibly participants regulate their affect, variability in the use of regulatory strategies was operationalized using a standard deviation-based algorithm and considered categorically (i.e., lower, moderate, greater variability) to assess non-linear effects. Participants also provided data on relevant covariates and 24 allostatic load biomarkers (e.g., cortisol, blood pressure). In 2017–2021, these biomarkers were again collected. Linear regressions modeled betas (β) and 95 % confidence intervals (CI) examining associations of affect regulatory constructs with future allostatic load. In fully-adjusted models including initial allostatic load, general regulatory strategies were unrelated to future allostatic load. Yet, greater versus moderate affect regulation variability levels predicted lower allostatic load (β=−0.14; 95 %CI: −0.27, −0.01). Only among more educated participants, greater use of anger expression predicted lower allostatic load, while the reverse was noted with anger control (βexpression=−0.12; 95 %CI: −0.20, −0.05; βcontrol=0.14; 95 %CI: 0.05, 0.24). While general regulatory strategies appeared unrelated to allostatic load, greater variability in their use and anger-related strategies showed predictive value. Subsequent studies should examine these associations in larger, more diverse samples.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".