Measurement, Correlates, and Health Outcomes of Medication Adherence Among Seniors
Bibliographic record
Abstract
OBJECTIVE: To provide a comprehensive review of the literature on the measurement, correlates, and health outcomes of medication adherence among community-dwelling older adults. DATA SOURCES: Searches of MEDLINE, PubMed, and International Pharmaceutical Abstracts databases for English-language literature (1966-December 2002) were conducted using one or more of the following terms: elderly, adherence/nonadherence, compliance/noncompliance, medication/drug, methodology/measurement, and hospitalization. STUDY SELECTION AND DATA EXTRACTION: From the above search, studies of medication adherence in community-dwelling seniors were selected for review along with relevant publications from the reference lists of articles identified in the initial database search. DATA SYNTHESIS: Although several methods are available for the assessment of adherence, accurate measurement continues to be difficult. The available evidence suggests that polypharmacy and poor patient-healthcare provider relationships (including the use of multiple providers) may be major determinants of nonadherence among older persons, with the impact of most sociodemographic factors being negligible. There is little consensus regarding other determinants of nonadherence. Relatively few high-quality investigations have examined the associations between nonadherence and subsequent health outcomes. Available data provide some support for increased health risks with nonadherence. However, interventions to improve adherence have seldom demonstrated positive effects on health outcomes. CONCLUSIONS: There are few empirical data to support a simple systematic descriptor of the nonadherent patient. The inconsistencies across studies may be attributable, in part, to the inherent difficulties involved in the measurement of a behavioral risk factor such as nonadherence. Future research in this area would be strengthened by incorporation of detailed assessments of patient-reported reasons for nonadherence, the appropriateness of drug regimens, and the effect of nonadherence on health outcomes.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".