Factors associated with psychotropic drug use among community-dwelling older persons: A review of empirical studies
Bibliographic record
Abstract
BACKGROUND: In the many descriptive studies on prescribed psychotropic drug use by community-dwelling older persons, several sociodemographic and other factors associated with drug use receive inconsistent support. METHOD: Empirical reports with data on at least benzodiazepine or antidepressant drug use in samples of older persons published between 1990 and 2001 (n = 32) were identified from major databases and analyzed to determine which factors are most frequently associated with psychotropic drug use in multivariate analyses. Methodological aspects were also examined. RESULTS: Most reports used probability samples of users and non-users and employed cross-sectional designs. Among variables considered in 5 or more reports, race, proximity to health centers, medical consultations, sleep complaints, and health perception were virtually always associated to drug use. Gender, mental health, and physical health status were associated in about two-thirds of reports. Associations with age, marital status, medication coverage, socioeconomic status, and social support were usually not observed. CONCLUSIONS: The large variety of methods to operationalize drug use, mental health status, and social support probably affected the magnitude of observed relationships. Employing longitudinal designs and distinguishing short-term from long-term use, focusing on samples of drug users exclusively, defining drug use and drug classes more uniformly, and utilizing measures of psychological well-being rather than only of distress, might clarify the nature of observed associations and the direction of causality. Few studies tested specific hypotheses. Most studies focused on individual characteristics of respondents, neglecting the potential contribution of health care professionals to the phenomenon of psychotropic drug use among seniors.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| 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".