Individualized medication card assists the elderly with medication understanding and adherence
Bibliographic record
Abstract
Objective: In the outpatient setting, polypharmacy and poor communication among providers has left a gap in drug education involving the elderly population. Linking all of these providers with an accurate medication regimen may be the answer. Nurses can play a pivotal role in drug-related problems in the elderly population. The aim of this study is to use a liaison nurse to help educate the elderly population on the importance of adherence and understanding their medications by using an individualized medication card.Methods: This study used a descriptive study design. A convenience sample (n = 16) of patients, who were sixty-five years of age or older and were taking five prescription medications daily was employed. After collecting patient demographic information, a counseling session was performed with the patient on their medications using an individualized medication card. Data was then collected via two follow-up phone calls at two and four weeks after initial counseling session using a medication questionnaire and a set of five follow-up questions.Results: Sixteen participants received counseling on their medications and an individualized medication card to take home. Only thirteen participants were reached via phone call, two weeks after their initial counseling session. Of these thirteen, two had lost or forgotten their medication card. Eleven reported that the medication card had been helpful in understanding and adhering to their medications. Of the participants who kept their cards, all were satisfied with the counseling materials and methods used.Conclusions: The findings indicate that the elderly population who were taking five or more prescription medications showed benefit from counseling on their medications using an individualized medication card. Such a tool will help nurse practitioners guide elderly patients in medication education and adherence in future practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".