Self-Rated Health and Inflammation: A Test of Depression and Sleep Quality as Mediators
Bibliographic record
Abstract
OBJECTIVE: Despite its simplicity, single-item measures of self-rated health have been associated with mortality independent of objective health conditions. However, little is known about the mechanisms potentially responsible for such associations. This study tested the association between self-rated heath and inflammatory markers as biological pathways, and whether sleep quality and/or depression statistically mediated such links. METHOD: Eighty-six heterosexual married couples completed a standard measure of self-rated health, the Center of Epidemiological Studies-Depression Scale, and the Pittsburgh Sleep Quality Index. Participants also had blood drawn for determination of plasma levels of interleukin 6 and high-sensitivity C-reactive protein. The Monte Carlo method was used to construct confidence intervals for mediation analyses. RESULTS: Results indicated that poor self-rated health was associated with higher CRP levels (B = .31, SE = .14, p = .028). Importantly, the Monte Carlo mediational analyses showed that these results were statistically mediated by sleep quality (aXb = 0.10, 95% confidence interval = 0.003 to 0.217) but not depressive symptoms (aXb = 0.03, 95% confidence interval = -0.03 to 0.10). CONCLUSIONS: These results highlight the biological and behavioral mechanisms potentially linking self-rated health to longer-term health outcomes. Such work can inform basic theory in the area as well as intervention approaches that target such pathways.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".