Rural Residence and Prescription Medication Use by Community‐Dwelling Older Adults: A Review of the Literature
Bibliographic record
Abstract
CONTEXT: Due to various barriers to health care access in the rural setting, there is concern that rural older adults might have lower access to prescribed medications than their urban counterparts. PURPOSE: To review published research reports to determine prevalence and mean medication use in rural, noninstitutionalized older adults and assess whether rural-urban differences exist. METHODS: PubMed, Ageline, Cinahl, PsycInfo, International Pharmaceutical Abstracts, Agricola, and Institute for Scientific Information Web of Science - Social Science Index were searched. English-language articles through May 2005 involving a sample of rural, noninstitutionalized older adults and analyses of overall medication prevalence and/or intensity were included. Review articles, conference abstracts, dissertations, books, and articles targeting nonprescription or specific therapeutic categories were excluded. A total of 206 citations were identified and 26 met the inclusion criteria. FINDINGS: Reported prevalence of prescription medication use by rural older adults varied between 62% and 96%, with 2-6 prescriptions per person. Multivariate analyses results were equally inconsistent. Controlling for insurance, most US studies suggest there is no rural-urban difference in access to prescribed medications. However, this finding may not be generalizable across all regions in the United States or other countries. CONCLUSIONS: Geographic location may not be as important a variable for medication usage as for other health services utilization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".