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Record W1987853702 · doi:10.1097/psy.0b013e3182732dc6

Associations Between Health-Related Self-Protection, Diurnal Cortisol, and C-Reactive Protein in Lonely Older Adults

2012· article· en· W1987853702 on OpenAlexafffund
Rebecca Rueggeberg, Carsten Wrosch, Gregory E. Miller, Thomas W. McDade

Bibliographic record

VenuePsychosomatic Medicine · 2012
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsConcordia University
FundersCanadian Institutes of Health Research
KeywordsLonelinessLongitudinal studyMediationCortisol awakening responseMedicineC-reactive proteinPsychologyInternal medicineDemographyHydrocortisoneGerontologyPsychiatryInflammation

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to examine whether health-related self-protection (e.g., using positive reappraisals or avoiding self-blame) prevents lonely older adults from exhibiting increases in diurnal cortisol secretion and higher levels of C-reactive protein (CRP). METHODS: This longitudinal study (n = 122) examined diurnal cortisol levels (area under the curve) at baseline and 2-year follow-up. Levels of CRP were measured at 6-year follow-up. The main predictors included baseline levels of loneliness and health-related self-protection. RESULTS: Among lonely participants, baseline self-protection predicted an amelioration of 2-year increases in diurnal cortisol volume (β = -.34, p = .03) and lower levels of CRP at 6-year follow-up (β = -.42, p = .006). These significant associations were not found among nonlonely participants (β < .14, p = .33). In addition, mediation analyses demonstrated that the buffering effect of self-protection on lonely older adults' levels of CRP at 6-year follow-up was statistically mediated by 2-year changes in cortisol volume (β = -.16, p = .06). CONCLUSIONS: These findings suggest that lonely older adults may ameliorate biologic disturbances if they engage in self-protection to cope with their health threats.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.312
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2012
Admission routes2
Has abstractyes

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