DYNAMICS OF DAILY LONELINESS AND DIURNAL CORTISOL
Bibliographic record
Abstract
Abstract Loneliness impedes healthy aging. Previous research shows robust findings between loneliness and poor health, including stress-related diseases and conditions (e.g., cardiovascular disease). Less is known about how loneliness experienced in daily life relates to indices of health, such as diurnal cortisol level patterns. The slope of diurnal cortisol shows a peak upon waking from sleep and a nadir close to bedtime; however, specific rhythms of cortisol slopes fluctuate in daily life. Emerging research suggests that capturing the dynamic range of diurnal cortisol in daily life could be an important marker of allostatic load (i.e., the cumulative burden of chronic stress), with a compressed range indicating dysregulation and a wider range suggesting a more adaptive cortisol profile. In the current research, we investigated whether daily self-reported loneliness related to the dynamic range of diurnal cortisol (calculated via 4 days of salivary cortisol collection, 4 times a day) and explored age differences across the lifespan. Data come from a national daily assessment study with participants ranging between 25-75 years old who completed 8 days of daily diary assessments on loneliness and other factors in daily life. Findings from the current analyses will be presented in the context of how the dynamic range of cortisol might serve as a potential mechanism for outcomes related to loneliness in later life.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".