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Record W4380364797 · doi:10.3390/ijerph20126102

Neuroticism and Stress in Older Adults: The Buffering Role of Self-Esteem

2023· article· en· W4380364797 on OpenAlexafffund
Maya E. Amestoy, Danielle D’Amico, Alexandra Fiocco

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsToronto Metropolitan UniversityThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsNeuroticismPsychologyStressorCoping (psychology)DistressClinical psychologyTraitPersonalitySelf-esteemBig Five personality traitsSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic stress is associated with accelerated aging and poor health outcomes in older adults. According to the Transactional Model of Stress (TMS), distress is experienced when one perceives the stressor, or threat, to outweigh the ability to cope. The experience of distress is correlated with trait neuroticism, which is associated with greater perceptions of stress and stress reactivity, as well as a tendency to engage in maladaptive coping strategies. However, as individual personality traits do not act in isolation, this study aimed to investigate the moderating role of self-esteem in the relationship between neuroticism and distress using a TMS framework. METHODS: A total of 201 healthy older adults (Mage = 68.65 years) completed questionnaires measuring self-esteem, neuroticism, perceived stress, and positive coping. RESULTS: = 0.06). No moderating effect was found for perceived stress or overall distress. CONCLUSION: The results support the association between trait neuroticism and indices of stress and suggest a potential buffering effect of self-esteem in moderating the negative association between neuroticism and positive coping.

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)0.002
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 score0.249
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.054
GPT teacher head0.410
Teacher spread0.356 · 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

Citations18
Published2023
Admission routes2
Has abstractyes

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