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Record W1499814643 · doi:10.1002/gps.3912

The association between self‐reported daily hassles and cortisol levels in depression and anxiety in community living older adults

2012· article· en· W1499814643 on OpenAlexafffund
Helen‐Maria Vasiliadis, Hélène Forget, Michel Préville

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2012
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsUniversité du Québec en OutaouaisHôpital Charles-Le MoyneUniversité de Sherbrooke
FundersFonds de recherche du QuébecSanten
KeywordsAnxietyDepression (economics)PsychologyStressorClinical psychologyAssociation (psychology)Psychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to assess whether the association, in a naturalistic setting, between daily hassles and diurnal salivary cortisol differs in the presence of depression and anxiety in older adults. METHODS: Data were assessed in a large representative community sample of older adults (n = 1760). A multinomial analysis was used to study as an outcome variable: no disorder, depression only, anxiety only and depression and anxiety, as a function of daily hassles and cortisol levels controlling for age, gender and time of saliva collection. Multivariate regression analyses were also carried out to test the association between daily hassles and cortisol levels stratified by depression and anxiety status. RESULTS: A significant positive association was observed between the number of daily hassles reported and cortisol levels in participants with no depression and no anxiety and in participants with anxiety. Participants without depression and anxiety, and those with depression only, had significant lower cortisol levels later in the day. This was not observed in respondents with anxiety. CONCLUSION: Stressors such as daily hassles are associated with cortisol secretion in depression and anxiety in older adults in a large epidemiologic setting.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.007
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.279
Teacher spread0.267 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

Explore more

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