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Record W4401193970 · doi:10.1177/21582440241267141

Investigating Stress Sensitization and Steeling for Early-Life Adversity and Recent Stressful Life Experiences: Health and Illness in Older Adulthood

2024· article· en· W4401193970 on OpenAlexaff
Myriam V. Thoma, S. Balsiger, Jan Höltge, Shauna L. Rohner

Bibliographic record

VenueSAGE Open · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychologyMental healthDevelopmental psychologyPhysical healthLife course approachStress (linguistics)Life spanGerontologyClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Early-life adversity (ELA) and recent stress experiences are relevant explanatory factors in the understanding of health differences across the life span. However, their particular role in explaining the vast health heterogeneity in older adulthood has yet to be defined. To address this gap, this study examined (a) the health of older individuals with differing levels of ELA and recent stressful experiences; and (b) the type (i.e., linear, curvilinear) of the expected stress-health relationships. Longitudinal quantitative data were collected on health, ELA, and stressful life experiences of the previous 21 months in N = 216 participants ( M age = 69.8 years, 45.8% female). Findings support linear (rather than curvilinear) stress-health relationships for ELA and recent stress with physical and mental health. Furthermore, ELA significantly moderated the relationship between recent stress and physical illnesses. As the detrimental health impact of ELA can still be detected in older adulthood, ELA may be critical for understanding later life health heterogeneity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.359
Teacher spread0.320 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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