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Record W4395094939 · doi:10.1093/geronb/gbae071

Pain Lowers Subjective Survival Probabilities Among Middle-Aged and Older Adults

2024· article· en· W4395094939 on OpenAlexaff
Gillian Fennell, Margarita Osuna, Jennifer Ailshire, Anna Zajacova

Bibliographic record

VenueThe Journals of Gerontology Series B · 2024
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsWestern University
FundersNational Institute on AgingNational Institutes of Health
KeywordsGerontologyPsychologyDemographyDevelopmental psychologyClinical psychologyMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

OBJECTIVES: Pain is a leading cause of disability and a limiting factor in individuals' assessments of their own subjective health; however, its association with subjective longevity has yet to be explored. Subjective survival probabilities (SSPs), or one's own perceived chances of living to a given age, can influence individuals' behavior as they plan for their futures. This study assesses whether pain correlates to lower SSPs. METHODS: We use a repeated cross-section of the 2000-2018 waves of the Health and Retirement Study, a longitudinal and nationally representative survey of Americans aged 51 and older (N = 31,773). RESULTS: Fractional logit regressions indicate that, across all age groups, respondents with severe and/or interfering pain reported significantly lower SSPs than those with no pain (Marginal Effect [ME] = -0.03 to -0.06, p < .05). Controlling for all covariates, mild or moderate noninterfering pain was only associated with a significant reduction in SSPs among the youngest group reporting their chances of living to age 75 (ME = -0.02, p < .001). Descriptively and in the model results, respondents with mild or moderate noninterfering pain appeared to more closely resemble pain-free respondents than those with severe or interfering pain. DISCUSSION: These findings highlight the importance of pain on SSPs, and contribute to the growing evidence that pain interference is uniquely important in predicting meaningful health outcomes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.768

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.303
Teacher spread0.271 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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