Is more, better? Relationships of multiple psychological well-being facets with cardiometabolic disease.
Bibliographic record
Abstract
OBJECTIVE: Cardiometabolic disease (CMD) is a leading cause of death and disability worldwide. Assessments of psychological well-being taken at one time point are linked to reduced cardiometabolic risk, but psychological well-being may change over time and how longitudinal trajectories of psychological well-being may be related to CMD risk remains unclear. Furthermore, psychological well-being is a multidimensional construct comprised of distinct facets, but no work has examined whether sustaining high levels of multiple facets may confer additive protection. This study tested if trajectories of four psychological well-being facets would be associated with lower risk of self-reported nonfatal CMD. METHOD: Participants were 4,006 adults aged ≥50 years in the English Longitudinal study of Ageing followed for 18 years at biyearly intervals. Psychological well-being facets were measured in Waves 1-5 using subscales of the Control, Autonomy, Satisfaction, and Pleasure scale. Latent class growth modeling defined trajectories of each facet. Incident CMD cases were self-reported at Waves 6-9. Cox regression models estimated likelihood of incident CMD associated with trajectories of each facet individually and additively (i.e., having persistently high levels on multiple facets over time). RESULTS: After adjusting for relevant covariates, CMD risk was lower for adults with persistently high versus persistently low levels of control and autonomy. When considering potential additive effects, lower CMD risk was also related to experiencing persistently high levels of ≥2 versus 0 psychological well-being facets. CONCLUSIONS: Findings suggest having and sustaining multiple facets of psychological well-being is beneficial for cardiometabolic health, and that effects may be additive. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".