Self-esteem variability predicts arterial stiffness trajectories in healthy adolescent females.
Bibliographic record
Abstract
OBJECTIVE: There is mounting evidence that high levels of self-esteem are associated with better health outcomes, particularly in older adults dealing with serious medical illnesses. Much less is known about how this linkage unfolds developmentally, particularly during times like adolescence, when youngsters' self-views are typically in flux. Here we explore the self-esteem of adolescent females over a 2.5-year period, and how it covaries with trajectories of vascular function assessed over the same timeframe. METHOD: One-hundred and thirty adolescent females completed the Rosenberg Self-Esteem scale every 6 months for 2.5 years. Vascular function was measured three times over the same period, using peripheral artery tonometry. Indices of endothelial function and arterial stiffness were derived from these measurements. RESULTS: Hierarchical Linear Modeling revealed an association between self-esteem variability and arterial stiffness trajectories, β = 9.0 × 10-3, SE = 4.4 × 10-3, p = .04. To the extent that their self-esteem fluctuated over the 2.5-year study, participants showed increasing trajectories of arterial stiffness, independent of various demographic and biobehavioral confounders. This association was also independent of participants' trait-like self-esteem over the same period of time. Neither trait self-esteem nor self-esteem variability was related to endothelial function. CONCLUSION: These findings suggest that fluctuating self-esteem may accelerate the early stages of vascular stiffening in young women, regardless of whether self-views are generally positive or negative.
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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 | 0.000 | 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 | 0.000 | 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".