Predictors of Older Adults’ Capacity for Medication Management in a Self-Medication Program
Bibliographic record
Abstract
UNLABELLED: The aim of this project was to identify variables that predicted older adults' ability to manage medications. METHODS: The study used a retrospective cohort design and was set in a self-medication program within a rehabilitation hospital. A random sample of charts from 301 participants in the self-medication program was reviewed. RESULTS: Logistic regression models accounted for 26.7% and 55.8% of the variance in the probability of making one or more self-medication errors during the initial and final weeks of the program, respectively. The importance of cognition in predicting medication management capacity was seen in bivariate and multivariate analyses and through a number of interactions with other predictors. Statistically significant predictors in one or both analyses included medication regimen complexity, Mini-Mental State Exam (MMSE) score, duration of institutionalization, depression, and interactions between (a) medication regimen complexity and MMSE score and (b) ability to cook and MMSE score. DISCUSSION: The direct effects of cognition and medication regimen complexity were important predictors of medication management capacity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".