Nurse-Led Strategies to Enhance Medication Adherence in Older Patients after Hospital Discharge
Bibliographic record
Abstract
Discharged older adult inpatients are often administered a variety of drugs. However, many only take roughly half of their medications and many discontinue treatment. Nursing strategies might enhance medication adherence in this group. The goal of this research is to assess the efficacy of nurse-led transitional care strategies after hospital discharge of older patients versus usual care in enhancing cognitive processes, physical performance, signs of depression and stress, perceptions of social support, patient satisfaction, and the costs associated with medical service use among older patients with multiple chronic conditions and signs of depression. Three sites in Ontario, Canada were used for a pragmatic multi-site randomized controlled research. Individuals were randomly assigned to either an intervention group or a control (normal care) group. 127 people over the age of 65 were discharged from the hospital with several chronic conditions and signs of depression. Over six months, a Registered Nurse provided individualized care through cell phone follow-up, house visits, and device navigation help as part of an evidence-based, patient-centered intervention. The main result was a shift in cognitive performance between the first and sixth months. Alterations in physical performance, depressed symptoms, stress, and social support perceived, patient satisfaction, and the cost of health care usage were secondary results measured from baseline to six months. ANCOVA modeling was used for the intention-to-treat analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".