Herbal Remedy Use as Health Self-Management Among Older Adults
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".