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Record W2153941515 · doi:10.1093/geronb/gbr070

Pain and Depression in Late Life: Mastery as Mediator and Moderator

2011· article· en· W2153941515 on OpenAlexaff
Arlene S. Bierman

Bibliographic record

VenueThe Journals of Gerontology Series B · 2011
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Calgary
FundersNational Institute on Aging
KeywordsModerationPsychologyDepression (economics)Depressive symptomsClinical psychologyDevelopmental psychologyPsychiatrySocial psychologyAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examines how mastery mediates and moderates the relationship between pain and depression among older adults, as well as the extent to which these processes differ by the timing of pain in late life, while utilizing statistical methods that comprehensively control for time-stable confounds. METHODS: Data are derived from multiple observations of adults aged 65 years and older in the Washington, DC, metropolitan area over a 4-year period. Fixed effects models are used to control for time-stable influences. RESULTS: With all time-stable influences controlled, pain is positively related to symptoms of depression, although this relationship is substantially reduced in comparison with a model in which all time-stable confounds are not held constant. Mastery does not mediate this relationship because pain is not significantly related to mastery once time-stable factors are taken into account. Mastery buffers the relationship between pain and depression, but only for elders later in late life. DISCUSSION: This study suggests that a synthesis of stress process and life course perspectives is critical for understanding how pain influences depression in late life. However, research that does not comprehensively control for time-stable factors may overestimate the consequences of pain for older adults.

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 score0.072
Threshold uncertainty score0.735

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 head0.095
GPT teacher head0.356
Teacher spread0.262 · 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
Published2011
Admission routes1
Has abstractyes

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