MétaCan
Menu
Back to cohort
Record W2607000269 · doi:10.1177/0164027517704970

Chronic Pain and Psychological Distress Among Older Adults: A National Longitudinal Study

2017· article· en· W2607000269 on OpenAlexaff
Alex Bierman, Yeonjung Lee

Bibliographic record

VenueResearch on Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAnxietyDepression (economics)Ethnic groupAssociation (psychology)Chronic painPsychological distressClinical psychologyPain catastrophizingPsychologyLongitudinal studyDistressSSS*PsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

This research examines whether unobserved time-stable influences confound the association between chronic pain and psychological distress in older adults as well as how race and ethnicity combine with subjective social status (SSS) to modify the association. In a nationally representative longitudinal survey, holistically controlling for unobserved time-stable influences using fixed-effects models substantially reduces the pain-depression relationship and eliminates the overall pain-anxiety relationship. The association with depression is stronger for Black and Hispanic elders, illustrating a process of double-jeopardy. Black elders with severe pain experience lower anxiety, as do Black elders with moderate pain and low SSS, which we suggest may be due to the enervating effects of undermanaged pain. Black elders at high SSS experience greater anxiety with moderate pain. This research suggests that undermanagement of chronic pain among racial and ethnic minorities differentiates the association between pain and distress in late life and especially creates stronger associations with depression.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.115
GPT teacher head0.482
Teacher spread0.367 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueResearch on AgingSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207