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Record W2106251483 · doi:10.1093/geronb/62.2.s142

Herbal Remedy Use as Health Self-Management Among Older Adults

2007· article· en· W2106251483 on OpenAlexaboutno aff
Thomas A. Arcury, Joseph G. Grzywacz, Ronny A. Bell, Rebecca H. Neiberg, Sara A. Quandt

Bibliographic record

VenueThe Journals of Gerontology Series B · 2007
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
FundersNational Center for Complementary and Integrative Health
KeywordsEthnic groupMedicineGerontologyHerbQuarter (Canadian coin)Traditional medicineInclusion (mineral)Medicinal herbsPsychologyGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: Guided by the self-regulatory model, we describe the proportions of older adults who include herbal remedies in their health self-management, determining differences in herb use in terms of personal and health characteristics, indicators of culture, and personal resources. METHODS: Data were from the 2002 National Health Interview Survey, which included a supplement on the use of herbal remedies. We limited the present analysis to adults aged 65 and older who were Black, Hispanic, Asian, or White. RESULTS: Herbs were an important component of the health self-management of older adults. Whereas about one quarter of Asian and Hispanic elders used herbal remedies, about 10% of Black and White elders used them. Older adults differed by ethnicity in the herbs they used and their reasons for using herbs. Predictors of herb use included gender, age, and health status. Ethnicity and region of the country, indicators of culture, and education, a personal resource, were significant predictors of herb use when personal and health characteristics were controlled. DISCUSSION: A complex set of factors is associated with the inclusion of herbs in the health self-management of older adults, with cultural and personal resources being extremely important.

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)0.000
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 score0.080
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.048
GPT teacher head0.368
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 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

Citations80
Published2007
Admission routes1
Has abstractyes

Explore more

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